Steering Diffusion Models Towards Credible Content Recommendation
Authors: Zhuo Cai
Shoujin Wang
Jin Li
Peilin Zhou
Victor W. Chu
Fang Chen
Tianqing Zhu
Charu C. Aggarwal
Venue: ICLR 2026 Poster (2026)
Keywords: Credible content recommendation
Societal Considerations
Diffusion models
Primary area: alignment, fairness, safety, privacy, and societal considerations

Abstract: In recent years, diffusion models (DMs) have achieved remarkable success in recommender systems (RSs), owing to their strong capacity to model the complex distributions of item content and user behaviors. Despite their effectiveness, existing methods pose the danger of generating uncredible content recommendations (e.g., fake news, misinformation) that may significantly harm social well-being, as they primarily emphasize recommendation accuracy while neglecting the credibility of the recommended content. To address this issue, in this paper, we propose Disco, a novel method to steer diffusion models towards credible content recommendation. Specifically, we design a novel disentangled diffusion model to mitigate the harmful influence of uncredible content on the generation process while preserving high recommendation accuracy. This is achieved by reformulating the diffusion objective to encourage generation conditioned on preference-related signals while discouraging generation conditioned on uncredible content-related signals. In addition, to further improve the recommendation credibility, we design a progressively enhanced credible subspace projection that suppresses uncredible content by projecting diffusion targets into the null space of uncredible content. Extensive experiments on real-world datasets demonstrate the effectiveness of Disco in terms of both accurate and credible content recommendations.

Published as a conference paper at ICLR 2026
STEERING DIFFUSION MODELS TOWARDS CREDIBLE CONTENT RECOMMENDATION
Jin Li' Tianqing Zhu Charu C. Aggarwal*
Zhuo Cai'
Shoujin Wang
Peilin Zhou²
Victor W. Chul
Fang Chen'
University of Technology Sydney "oong Kong University of Science and Technology (Guangzhou)
City University of Macau
'IBM T. J. Watson Research Center
ABSTRACT
In recent years, diffusion models (DMs) have achieved remarkable success in rec- ommender systems (RSs), owing to their strong capacity to model the complex distributions of item content and user behaviors. Despite their effectiveness, ex- isting methods pose the danger of generating uncredible content recommendations (e.g., fake news, misinformation) that may significantly harm social well-being. as they primarily emphasize recommendation accuracy while neglecting the cred- ibility of the recommended content. To address this issue, in this paper, we pro- pose D Disco, a novel method to steer diffusion models towards credible content recommendation. Specifically, we design a novel disentangled diffusion model mitigate the harmful influence of uncredible content the generation process while preserving high recommendation accuracy. This is achieved by reformu- lating the diffusion objective to encourage generation conditioned preference- related signals while discouraging generation conditioned on uncredible content- related signals. In addition, to further improve the recommendation credibility, we design a progressively enhanced credible subspace projection that suppresses uncredible content by projecting diffusion targets into the null space of uncredible content. Extensive experiments on real-world datasets demonstrate the effective- ness of Disco terms of both accurate and credible content recommendations.
INTRODUCTION
Diffusion models (DMs) have achieved remarkable advances across multiple domains, such as image synthesis (Ho et al., 2020, Dhariwal & Nichol 202T) and language/text generation (Li et al.] 22022 Lovelace et al.) 2023). Owing to their strong capa- bility in modeling complex data distributions of user behaviors and diverse item content types (e.g., text, images, and videos), DMs have attracted growing at- tention in recommender systems (RSs), thereby fur- ther driving the innovations in this field (Wang et al.
A user's interaction sequence
Target item's embedding
Context items' embeddings
Sequence Encoder
Condition
Forward
Reverse
2023d Yang et al. 2023b Liu et al. 2025a).
e
DM-based recommendation methods generally adopt a diffusion-theen-deniiing paradigm model the distributions of users' behaviors and then gener- ate items they are likely to engage with Yang et al.) 2202bb Liu et al.) 2025a Li et al.] 2023). Figure illustrates the overall process of existing DM-based methods. A sequence encoder (e.g.. Transformer (Vaswani et al. 2017). GRU (Chung et al., (0044) is first employed to encode the embeddings of the first (n f 1) context items interacted by user into unified representation of the user's overall preference, which serve as the condition in the reverse stage. The n-th item (i.e., the last item
Figure general paradigm of DM-based sequential recommendation methods. The condition and diffusion target (i.e., target item's 's embedding) are two core components in DM-based methods.
"Corresponding author; Shoujin. Choujin.Wangeee Slan@@uts.cdu.au

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Published as a conference paper at ICLR 2026
interacted with by the user) is then treated as the diffusion target. In the forward process, noise is gradually added to the diffusion target according to a predefined noise schedule. In the reverse process, the sequence encoder's 's output serves the condition to guide the generation of the item embeddings that reflect users' genuine preferences (Li et al. 2025b; Cai et al. 2025).
Although DM-based recommendation methods have achieved remarkable success, they often over- look a critical real-world concern: the risk of generating uncredible content recommendations that can harm social well-being. For example, news RSs powered by DMs may produce uncredi- ble recommendations containing fake news [Wang et al. 20226 2024a), as these methods typically overlook the credibility of recommended content. Recommending such uncredible content to users not only diminishes users' experience but also poses substantial societal risks. For instance, during the COVID-19 pandemic, news RSs (e.g., online news portals) Wang et al.] (2023b); (Zhao et al. (223)) were shown to amplify the spread of health-related fake news (e.g., false cures and vaccine conspiracy theories), which fueled public confusion and resistance to medical treatments (Loomba et al. 2021 Hence, from societal consideration perspective Wang et al. (2024b); Li et al. (2024); Wang et al. (2022a), it is crucial to develop DM tailored for credible content recommendations
To achieve this goal, we first conduct both empirical and theoretical analysis to investigate why existing DM-based recommendation methods risk generating uncredible content recommendations. The detailed analysis can be found in Appendix C Our analysis reveals two key factors: (1) un- credible condition, which arises when a user has previously interacted with uncredible items (i.e., there are uncrdible items in context items); and (2) uncredible diffusion target, which occurs when the target item itself is uncredible. In this paper, an uncredible item refers to an item containing uncredible content, such fake news and misinformation. These two factors jointly lead existing DM-based methods to generate recommendations that may contain uncredible content.
Hence, to develop a diffusion model tailed for credible content recommendation, carefully address these two factors. A straightforward solution is to remove uncredible items from both context items and diffusion targets. or to apply recommendation unlearning methods (Chen et al. [202]] Zhang et al., 2024b] Li et al. 2026) to erase their impacts. This can ensure the credibil- ity of both the condition and the diffusion target. However, such an approach raises a critical issue: uncredible items may still reflect users' genuine preferences. For instance, if user reads a sports- related fake news article, it may signal this user's underlying interest in sports topics. In this case, removing the uncredible items entirely would severely harm recommendation accuracy. Thus, the first challenge lies in how to mitigate the negative impact of uncredible content without sac- rificing recommendation accuracy. An alternative solution is retain users' preference-related information while removing only the uncredible aspects of content items. However, this approach requires rich supervision (i.e., credibility labels) to ensure accurate and comprehensive removal. In practice, only a small portion of items are verified and labeled. For example, on news portals, some articles may be flagged as fake, while many others remain unverified. Hence, the second chal- lenge is how to develop a diffusion model that can effectively handle both known and unknown uncredible content under limited label availability. Existing methods for credible content recom- mendation (Wang et al. 2022b] 2024a Ma et al. 2025) typically assume that all uncredible items are fully labeled, which rarely holds in world scenarios, leading to suboptimal performance.
necessary to
To overcome these two challenges and steer diffusion models towards credible content recommendation, we propose a novel framework called Disco. Specifically, to address the un- credible condition and the challenge of preserving recommendation accuracy, we design a dis- entangled diffusion model that separates uncredible content from users' preference-related informa- tion in items' embeddings. With this disentanglement, the generation process becomes free from the harmful influence of uncredible items, while still preserving high recommendation accuracy by retaining users' genuine preference-related information. In addition, instead of incorporating auxil- iary disentanglement networks and constraints which often introduce extra computation cost (Wang al. 2023e] Qi et al. 2024, Wang et al. 2022b Ma et al. (025), the diffusion model itself can serve as an effective disentangler with proper adjustments. Specifically, we reformulate the diffu- sion objective encourage the model generation guided by preference-related signals (i..e, signals indicating users' preference, such as content topies), while discouraging the generation conditioned on uncredible content-related signals (i.e., uncredible signals such as inaccurate and misleading information). To address the uncredible diffusion target, we introduce credible subspace pro- jection module to project diffusion targets into the null space of uncredible content features, which maximally excludes uncredible information. To overcome the challenge of limited labeled data,
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The content recommendation task in this paper follows the sequential recommendation paradigm (Wang al. 2019a Zhang et al. 2018), which aims to infer users' potential interests based on their chronologicaly ordered interaction sequences with content items (e.g., news, and videos and movies) (Wu al. 2023a Deldjoo al. 2016; Goyani & Chaurasiya, 2020). The set all sequences is denoted as represented as = {iz,.... 1n ) (s S). Here, {ir.... - are the context items, and i, the target item. Each content item i; is transformed into embedding vector C₃ using modality- specific feature extractors, such as language models for textual content or visual encoders for images and videos, yielding a sequence of embeddings (e.,... e. Given user's historical sequence s, the goal is to generate a personalized ranking over a set of candidate content items and predict the Published as a conference paper at ICLR 2026
the uncredible content features are progressively enhanced by detecting and incorporating potential uncredible items, making the null space projection progressively more accurate and comprehensive. Comprehensive experiments verify the effectiveness of Di Disco in terms of delivering both accurate and credible content recommendations.
In summary, contributions be concluded follows:
We propose Disco, a novel diffusion model tailored for credible content recommendation. To the best of our knowledge, Disco is the first work designed for credible content recommendation under conditions of limited credibility labels.
A novel disentangled diffusion model is designed to mitigate the recommendations of uncredible content while preserving high recommendation accuracy.
We propose new progressively enhanced credible subspace projection further suppress and mitigate the harmful impacts uncredible content contained in diffusion targets.
Comprehensive experiments on three real-world datasets demonstrate the effectiveness of Disco in generating both accurate and credible content recommendation.
2
PRELIMINARY
2.1 CREDIBLE CONTENT RECOMMENDATION
Content recommendation.
[81, 82, s1Sss, where each sequence is
next item that the user is most likely to engage with (Kang & McAuley 2018).
Credible content recommendation. In this paper, we formulate the task of credible content rec- ommendation mitigating the exposure of users uncredible items (Wang et al. 2022b 2024a). A recommendation model is considered more credible if its generated recommendation lists con- tain smaller proportions of uncredible items. uncredible items include uncredible information like fake news and misinformation Zhang et (2025), ) which often degrades user experience and leads to adverse societal impacts. Moreover, we focus on more challenging setting which only par- tial credibility labels indicating whether an item contains uncredible content are available during training. reflecting the practical difficulty of obtaining exhaustive annotations in real-world RSs. In contrast, complete labels are provided during testing to ensure accurate evaluation.
Definition 1 Content credibility. Content credibility indicates whether item contains uncredible information such as false, misleading, or inaccurate content. Items containing such information are regarded uncredible (e.g., fake news, misinformation), whereas all others are regarded credible.
2.2 DIFFUSION MODELS FOR SEQUENTIAL RECOMMENDATION
In sequential recommendation scenarios, DMs are generally utilized on the embedding of the last item (i.e., C.,) sequence (Yang et al., 2023b Liu et al.) 2025a). The detailed process is as follows:
In the forward stage. DMs gradually add Gaussian noise to embedding acoording to noise schedule [31.... Br]]
g( = NNee /1 -,,e 181),,
q(e = NNee, Vare (1 I)) (1)
where el en ar = Br and âr IT= Os. The first equation the step-by-step Markov process from e/-1 e/, The second equation is derived based on the Markov chain principle (Ho
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al. 2020), which can be used directly derive ex, from ef, in one step. A reparameterizzation trick is then applied to obtain variable ex vāre, + vI Oye, where (O,,).
In the reverse stage. DMs progressively recover the diffusion target step by step starting from Gaussian noise p(e"") = (0,II))
po -e c) =N(e po c, 1), [o c, ()),
where Eg Eg , C, t) is fixed to (t) = 1- 1 3B following the common practice in previous work Yang et al. 2023b Wang et al., 2023d). M c,t) is the predicted mean from a network fo(-): (ar(1-ar-1) c + v fa. 11 (1-aa), fo c,t). In DM-based recommendation methods, vi-a;
1-a;
pe (e c, fo is generally implemented as an MLP for efficiency.
1-a;
The denoising process is guided by preference condition constructed from the context items (fe).... Cp }) using a sequence encoder (e.g., Transformer (Kang McAuley, 2018), GRU
Hidasi et al. 2015)). This condition represents users' overall preference.
Optimization. The core of DM-based sequential recommendation is to optimize the conditional data generation distribution (e c),, which is performed by optimizing the variational bound negative log likelihood as follows:
E - log pa (e(cc)) Eg -log
Po n
c) ee0
= C.
1: (
3 THE DISCO MODEL
In this section, we first introduce our disentangled diffusion model (Section 3.1 followed by the projection of diffusion targets into a credible subspace (Section 3.2). These two components jointly enable the learning of credible conditions and credible diffusion targets (i.e., two essential elements in diffusion models) to guide the model toward credible generation. Subsequently, to address the more realistic scenario where only a limited portion of content items are labeled with credibility information, we propose a progressive enhancement mechanism for the credible subspace (Section 3.3 Thereafter, present the overall optimization objective of our proposed model, which in- legrates a content disentanglement term and preference contrast term to simultancously enhance recommendation credibility and accuracy (Section 3.4). Finally, detail the credible generation and recommendation process after training (Section B.5). All components are interlocked to con- struct unified diffusion-basee framework for accurate and credible content recommendation under limited credibility supervision. The pseudo-codes of our model are provided in Algorithms
3.1 DISENTANGLED DIFFUSION MODEL
Our disentangled diffusion model built upon two objectives: (1) generating item embeddings that reflect users' genuine preferences; and (2) reducing the negative influence of uncredible content on the item embedding generation process. To achieve these objectives, we guide DM to generate the item embeddings using the preference-related condition while discouraging the guidance by uncredible content-related condition.
To achieve this, we first introduce two content learners to extract user preference signals and uncred- ible content signals from items' embeddings. To ensure model simplicity and computational effi- ciency, both learners are implemented using MLP architectures. Formally, the preference-awe bedding erre and the uncredible content-aware embedding e'me are obtained via e = MLP (e) and eune MT.P (e). respectively. Accordingly, the context items in user's interaction se- quence can be transformed into two separate sequences: the preference-related sequence gre (ef"", .... _
em-
} and the uncredible content-related sequence g uuee = {el"c, e
Thereafter, construct preferencee-elaate and uncredible content-related conditions from corre- sponding embedding sequences through Transformer: ce = Transformer( ((e" -- and u Transformer( (e eone 1). We employ the same Transformer architecture with (Kang & McAuley 2018 Yang et al,, 2023b). However, applying the Transformer twice com- putationally expensive. Therefore, we replace the Transformer with mean pooling construct
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Disentangled Diffusion Model
Content item
Preference Content Leernrr
Sequence
Recommendation
Encoder
Embedding Layer
Generated embedding
Preference-related condition
Uncredible Content Leamer
Mean
Candidate items
Context
Pooling
items...
Uncredible content related condition
Matching score
calculation Mi Candidate items
Credible Subspace
Projection
Target item
Diffusion farget
Credible diffusion target
Credible Subspace Projection
Disentangled Diffusion Model
8' Tune
Null-space
Condition: t
Encourage: Preference -guided Diffusion
Credible diffusion target
Uncredible feature matrix
Null Null-space of uncredible feature matrix
Credible
Discourage: Uncredible content-quided Diffusion
projection
Condition:
eame
Figure 2: The overall framework of Disco. For simplicity and ease of understanding, the progres- sive enhancement the credible subspace is not shown in the figure.
credible content-related condition (i.e., cume = Mean((e)mc does not exhibit temporal dependencies.
e H),, since content credibility
After constructing the two conditions, Disco is optimized by jointly encouraging generation guided by preference-related condition cere and discouraging generation guided by uncredible content- related condition c Specifically, it minimizes the variational bound on the target item e,, when conditioned on c while maximizing the variational bound when conditioned on cuue
arg min Ey, -log pe O:T"
) q( [e:77 e.))
(e(e (anc) g(e
E,g
log
(4)
e
By employing this training objective, DM can naturally disentangle the two types of information without requiring additional components to explicitly enforce the separation. Importantly, we do not use are e as diffusion targets. Otherwise, disentanglement would be ineffective because the diffusion condition and target would lie in the same space, lacking meaningful disentanglement direction. Our ablation study in Section 4.3 further confirms this, showing that replacing e,, with efre or eunc significantly deteriorates recommendation performance.
The training objective in Equation 4 can reformulated as the following loss:
(5) The detailed derivation is provided in the Appendix D However, directly training the model with this loss can lead to severe instability. Specifically, the second term may converge to an extremely small value, causing the model predominantly optimize this term while neglecting the first term. To address this, inspired by (Liu et al., (202a)). we replace the MSE loss with a cosine loss:
L= = care,
Eere
[lle" fa(e), PTe [t)l Eeg. i [lle% fo(e",, cmT .01B]
-= Egg eare, [S] (e fo (o cere, c/re,t))) .t)] Egg [S (c fo unc (t)c,
(6)
where S(..)) = (1 - cos(,, 2)² and cos(-,-) is the cosine similarity of two embeddings. This loss preserves the same optimization direction that in Equation 5 while its values remain within stable range, thereby improving the stability of model training.
3.2 CREDIBLE SUBSPACE PROJECTION
The last item in a user's interaction sequence may also be an uncredible item, leading to an uncred- ible diffusion target. In such cases, optimizing Equation o might still be suboptimal for mitigating uncredible content. To address this, we design credible subspace projection operation, which projects the diffusion target into the credible subspace to suppress uncredible content.
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To achieve this, we first construct uncredible feature matrix F E Rluunelxd by stacking the uncredible content embeddings of all uncredible items, i.c., {eanali E Iune), where Iune is the set of uncredible items and d is the embedding size. The credible subspace projection is then performed by projecting the diffusion target into the null space of F, which serves as a subspace that maximally excludes uncredible content. Following prior work null-space projection (Fang al. 2025
Wang
ct al. 2021a
Hu et al. 2025) we apply Singular Value Decomposition (SVD) on F';
(7) where each column of left singular matrix U is an orthogonal basis of FT. V denotes the right singular matrix. A contains the corresponding singular values, which indicate the magnitude of uncredible information encoded by the orthogonal basis in U. A higher singular value corresponds to a orthogonal basis that is denser in uncredible information. Accordingly, we remove the submatrix U1 in U whose singular values exceed predefined threshold. The remaining submatrix, U2, consists only of orthogonal basis containing sparse or no uncredible information. The diffusion target is subsequently projected into the null space of F using the following operation:
{U,A, V] = SVD(F
é U2U
where č, is the credible diffusion target. To preserve the useful information contained in the original target item embedding e.. we adopt a residual connection to combine it with the projected embed- ding, yielding the responsible diffusion target as: ē. = (é, + e,) /2) This credible diffusion target is then used to replace the original target in the diffusion loss, as defined in Equation 6
L Egg care, [S (eh, So (o cPre, .))] Reg eune, [S (e fo(é) C. unc ,))]
where T = e. Training the model with this loss further enhances the credibility of recommenda- tion generation by projecting the diffusion target into more credible subspace.
3.3 PROGRESSIVE ENIIANCEMENT OF CREDIBLE PROJECTION
Owing to the second challenge mentioned in the introduction, the uncredible feature matrix F may capture only a limited set of uncredible features, leading to an incomplete credible subspace projec- tion. To address this, we propose a progressive enhancement strategy for credible projection.
Let Tune denote the set of items already labeled uncredible content, and the remaining items in I \ Tume have uncertain labels. Actually, there is still proportion of items in I Iune that are uncredible items but are not verified. In real-world scenarios, uncredible content often exhibits shared features. For instance, fake news articles tend to use emotionally charged or sensational headlines, such as those written in all capital letters In light of this, we try to detect the potential uncredible items by calculating the uncredible degree of items in I Iune:
UD(1)
cos(e) onc
.e
unC).
(10)
[unce
d'Ewnme
where e!tc and egnc are uncredible content embeddings of item i in I \ Iunc and item i' in Tune- -os(..)) calculates the cosine similarity between two embeddings. UD(i) represents the uncredible degree of item i, quantifying the likelihood that item in I Iune is an uncredible item. Items with the highest uncredible degrees are selected as potential uncredible items.
At the early stages of training. the disentangled diffusion model is not fully trained, resulting less accurate estimates of uncredible degrees. As training goes on, the model's capability improves. Therefore, instead of using a fixed selection ratio, we propose a progressive selection strategy. Specifically, we predefine maximum selection ratio y and linearly increase the selection ratio from zero to y after m training iterations. Consequently, the selection ratio at the j-th training it- eration given by ratio(j) = min(7 4 After calculating the current selection ratio, the top ||Z Tunel ratio(j)] items in I \ Iune with the highest uncredible degrees are selected as the potential uncredible items and added to the set Zune- Subsequently, the uncredible feature matrix F updated based on the expanded set Iunc- This update enhances the comprehensiveness of the null space of constructed uncredible content features, reduces residual uncredible features, and enables the diffusion target to be projected into more credible subspace.

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3.4 OVERALL OPTIMIZATION OBJECTIVE OF DISCO
The optimization loss in Equation 9 primarily addresses two objectives: capturing a user's positive preference (i.e., the target item) and enforeing content disentanglement. However, in RSs, modeling a user's negative preference is also crucial, as it enables the model to understand which types of interested in. To incorporate this objective and further enhance recommendation items formulate the final version of our diffusion loss with an additional preference contrast accuracy, we term by enlarging the distance between positive and negative preference:
Luisco (c fo(c, core, .t)) (ch, fo fo cune, ,t))
Content disentanglement
(11)
+ w (S (en Jole c ()) (eneg So Soleneg c ,)))
Preference contrast.
where exeg eang is the embedding of sampled negative preference item (i.e., item that user has not interacted with). w is hyperparameter controlling the contribution of each term, and t ~ T)) For simplicity, diffusion model to generate items reflecting users' positive preferences rather than negative prefer- ences. Although computing this loss requires multiple forward passes through fo, the computational overhead remains minimal, as fo is implemented as an MLP, which is effeceffiii.... Moreover, since all components share single fo network, additional memory consumption is required.
omit the expectation notation. The second term encourages the
3.5 CREDIBLE GENERATION AND CONTENT RECOMMENDATION
In this section, we describe the generation/inference process of Disco.
Following the generation paradigm Denoising Diffusion Probabilistic Models (Ho et al. 2020). the one-step generation procedure is defined follows:
c-1 V 1 ate) So(e) crre, ()+ Var(1 ai-1)
āi- (1 oz)
e +
(12)
1 Oz
The generation step begins with e ~ No(,,) We employ preference-related condition cere to guide the generation, ensuring the generated embeddings capture users' genuine preferences. This approach prevents the generated embedding from incorporating uncredible content features, even if users have previously interacted with uncredible items, thereby enhancing the credibility of the generation. To improve efficiency, we adopt the DDIM sampling strategy (Song et al. 2021).
The generated embedding e,, represents the user's predicted future preference. It is then used to compute matching scores with candidate items: ÿ = ef e," where e; is the embedding of candidate item i. The top-K items with the highest matching scores subsequently recommended to the user.
Discussion: Comparison between Disco and other DM-based methods.
Model architecture: DreamRedYang et al. (023b). DiffuRec Li et al. 2023). and PreferDiff Liu et al. (225a)) all adopt single-channel diffusion architecture, in which single condition is used to guide the generation of the target item. contrast, Disco employs a disentangled diffusion architecture with dual channels, leveraging two conditions to guide the generation. This design plays a crucial role in separating preference-related information from uncredible content signals.
Objective formulation: DreamRec uses the standard ELBO objective for diffusion models, whereas PreferDiff adopts variant ELBO combined with Bayesian Personalized Ranking (BPR) loss. DiffuRec instead uses a cross-entrops (CE) objective, essentially turning a gener- ative diffusion model into discriminative one. By contrast, model also belongs to variant the ELBO, but one specifically designed to achieve both accurate and credible generation an ability that DreamRec, DiffuRec, and PreferDiff do not possess.

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4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. We evaluate our method on three datasets: PolitiFact, GossipCop and MHMisinfo. The PolitiFact and GossipCop datasets are derived from FakeNewsNet repository (Shu et al. 2020). These datasets contain user-news interaction data, where fake news is treated as uncredible content items. Our task requires user-item interaction sequences together with credibility labels indicating whether the items are credible items not. To the best of our knowledge, these three datasets are the only publicly available datasets that meet these requirements, which can be used in our experiments. MHMisinfo is collected from video-based mental health misinformation datase Nguuen et al. 2025). This dataset contains users' interaction sequence with videos and the videos containing misinformation are uncredible items. Since this dataset does not provide video metadata but only textual descriptions, we use the textual descriptions as the item content. A detailed description of these datasets is provided in the Appendix [.1
?
Baselines. To evaluate the effectiveness of Disco, we compare it with four categories of sequential
recommendation methods: (1) Traditional methods, including GRU4Rec (Hidasi al. (015).
SASRec (Kang& & M
Bert4Rec (Sun et al. 2019), and LRURec (uue et al. 2024); (2)
Contrastive learning- based methods, including CL4SRec (Xie et al. 2022) and ContraRec (Wang)
et al. 2023a); (3) Credible recommendation methods. including Rec4Mit (Wang et al. 2022b),
HDlnt (Wang et al. 2024a), and PRISM (Ma et al.] (025); (4) DM-based methods, including
DreamRec (Yang a 2023b), DiffuRec (Li et al. 2023), PRISM (Ma et al., 2025). PreferDiff (Liu
ct al. 2025a). The details these methods are provided in the Appendix B.2
Evaluation Metries. We evaluate model performance using three types of metrics: accuracy- oriented metrics such as HR @ K and NDCG@K, a credibility-oriented metric CR@K (i.c., credible rate), and combined metric HC @
K that integrates HR @K and CR @ K. We follow the standard top-K evaluation protocol with K = 5, 10, as commonly adopted in sequential recommendation tasks (Kang& & McAuley 2018). Specifically, CR@K, K. proposed by (Wang et al. 2022b), measures the proportion of credible content items in the top-K recommendation list, where higher value indi- cates more credible output. The detailed definitions of these metrics are provided in Appendix [B.3
Implementation Details. During training, we assume that labels for 20% of randomly selected un- credible items are available, simulating the sparsity of labeled data in real-world scenarios. For fair comparison, we initialize each model's hyperparameters as suggested in the original papers and then fine-tune them on our datasets to ensure their best performances are reported. The hyperparameter w is tuned within {0.5, 1, 1.5, 2, 5), and y within 10.1, 0.2, 0.3, 0.4, 0.5}. while m is fixed at 10,000. The threshold for constructing the null space is fixed at 3. Model parameters are optimized using AdamW W (Loshchilov & Hutter (0077). Each method is run five times, and we report the average per- formance along with the standard deviation. Additional implementation details and hyperparameter settings are provided in the Appendix B.4
4.2 OVERALL PERFORMANCE COMPARISON
From the results reported in Table I we have the following observations:
Our proposed method, Disco, consistently outperforms competitive methods in both accu- rate and credible content recommendation. Disco achieves the best performance across all datasets and metrics. These results indicate that Disco can effectively reduce the recommenda- tions of uncredible content while maintaining high recommendation accuracy. This is enabled by the disentangled diffusion model and the progressively enhanced credible subspace projection. No- tably, Dis Disco excels in recommendation accuracy due to the incorporation of negative preference modeling, thereby better modeling users' genuine preference.
DM-based methods generally exhibit better recommendation accuracy than other approaches. Thanks to their strong ability to model complex distributions of user behaviors and item content, as well as to capture the inherent uncertainty in user behaviors, DM-based methods consistently
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Table 1: Overall performance comparison. The best performances are in bold, and the second-best
performances are underlined. The standard deviation is present in the form of percentage (%).
Datasets
Methods
HRØst
HR@10T
NDCGe5t
NDCG@ 10
CRØst
CR@10
HCe5*
HCe 10
GRU4RRec
0.2142 1051
0.3390 2064
0.1463 +0.37
0.1863 #0:41
0,9266
=0.84
0.9122 #0.76
0.2929 #0.1
0,3889 =0.51
SASRcc
0.2158
0,3519
0.1386
0.1823 50.10
0,9059
2041
0.9028
0.2929
0,3955
LRU
0.2168
0,3506 2025
0.1443 20.05
0.1872 50.13
0.8976
2020
0.8956
0,2924 20.14
0,3938
Bert4Rec
0.2191
0,3473 60
0.1472 10.05
0,1883 10.08
0,9172
s0.11
0.9045
0.2960 .0.09
0,3929 #0.13
CLASRcc
0.2247
0.3527 20.77
0.1508
0.1919 20.05
0,9132
20
0.9027
20.67
0.3012
0,3960
ContraRec
0.2241 +0.25
0.3512
#033
0.1508
0.1917 +0.17
0,8803
#2.7
0.8979
40.00
0.2969 +0.35
0.3941 #0.16
PolitiFact
Rec4Mit
0.2118 +0.28
0.3449
40.0
0.1413
0.1840 +0.15
0.8959
+0.73
0.8925
+0.59
0.2876
+0.30
0,3891 +0.25
HDiitt
0.2153 #0.27 0.3594
50.34
0.1272 10.19
0.1734 #0.21
0,8944
=0.31
0.8946
40.36
0.2906
#0.21
0,3985 #0.18
PRISM
0.1927 20.48 0.2758
2017
0.1348
#0.31
0.1615
20.25
09335
2002
0.9172
#1.21
0.2727
#0.47
0,3446 #0.26
DieamRoc
0.2416
11.88
0.3287
#1.94
0.1767 11.70
0.2047
11.69
0,8620
43:24
42,03 0.8437
0.3054
11.42
0,3664 a1.15
DiftuRec
0.2606
#1.21 0.3558
#1
0.1894
40.92
0.2214
11.08
0,9265
#1.60
0.9153
10.76
0.3334
a0.81
0.4027
20.85
PreferDiff
0,2531
+1.02
0.3554
+0.52
081818
+1.04
0.2144
+0.86
0.8925
+2.08
0.8981
+2,34
0.3228
+0,90
0,3968 +0.75
Diaco
0.2678
10.53
0,3775
.0.70
0.1983
s0.17
0.2336
s0.19
0.9823
a0.34
0.9425
.1.72
0.3466
+0.50
0.4192
a0.78
v-values
6.3e -
8.1e
2.9c -3
2.4e
7.8e
1.8e
1.8e
2.4e
GRU4Ree
0.2226
+2.44
0,3194
=3.10
0. 1466 11.83
0.1778
+1.94
0.8864
0.8706
1.660
0.2957
#2.61
0,3678
#2.61
SASRec
0.3078
10.19
0,4706
20.0
0. 1607 10.19
0.2135
10.14
0,8743
.1.7
0.8526
al.38
0,3612
20.49
0,4473
s0.41
LRURes
0.3316
10.18 0,5101
s0.11
0. 0.1697 +0.12
0.2276
+0,09
0.8544
#2.39
0.8439
+1.57
0,3732
+0.52
0,4618
=0.57
Bert4Re:
0.2372 +0.18
0,3711
-0.18
0. 1,3338 +0.15
0.1770
+0.14
0.8764
+2.00
0.8587
+0.74
0,3073
+1.08
0,3984 +0.12
CL4SRee
0.2898
10.39 0,4100
20:45
0,1784 +0.30
0.2174
20.30
0,8938
#004
0.8932
+1.50
0,3516
40.32
0,4275 40.39
ContraRec
0.2848
10.14 0,4224
60.16
0.1574 10.15
0.2020
10.19
0.8754
a2.06
0.8549
20.94
0,3450
a0.39
0,4249 20.20
GossipCop
Rco4Mit
0.2775 11.73 0,4403
11.98
0.1606
11.26
0.2133
11.28
20 0.8979
0.8649
11.39
0,3427
11.57
0,4360 20.89
HDInt
0.3407 +0.15
0,5249
=0.27
0.1748
+0.09
0.2345
+0.09
+0,91
0,4755 +0.25
PRISM
0.2948
+0.33
0,3447
0,2301 +0.30
0.2463
+0.29
0.8806
+3.00
0.8738
+1.63
0.3531
+0.70
0,3852 +0.42
DreamRee
20:08 0,5501
2013
0,3415 +0.05
0.3704
#007
0.8464
#3.77
11.95
0,4415
0,4742 #0.01
DiffuRee
0.4571
#043
0,5008
0,3887 +0.25
0.4029
#0.26
0.8313
50.58
0,4354
#0.19
0,4495
#0.34
PreferDiff
0.4969
0.6022
20007
0,3655
40.01
0.3999
40.02
0.8307
0.8228
0,4523
+1.1d
0,4887
Disco
10.80 0.5236
0.6143 20.66
0.3996 20.91
0,4292 s0.81
+0.35 0.9277
0,9039
+1.53
0.4918
+0.40
0.5207 20.00
p-values
1.4e
9.5e
5.5e
2.5e
9.8e
2.4e
1.7e 3
3.0e
GRU-4Ree
0.1151
14.47
0.1894
#2.47
0.0760
49.43
0,0998 11.71
0.8380
42.68
0.8608
12.38
0.1803
12.18
0,2624
42.86
SASRec
0.1485
20.39
0.2592
#1.31
0.0826
40.25
0,1179
20.26
0,8839
=1:24
0.8915
20.58
0.2190
20.56
0.3276
#1.21
LRURee
0.1571 10.77
0,2704
s0.39
0.0877
40.42
0.1268 s0.39
0,8359
.0.78
0.8818
10.76
0.2283
10.93
0,3350 e0.23
Bett4Ree
0.1391 10.70
0,2299
2003
0.0847 10.44
0,1138 10.53
0,8162
20.75
0.8786
10.80
0.2074
10,92
0,3017 11.08
MHMisinfo
CLASRec
0.1734 +0,50
0,2621
#0.3
0.1101
+0.30
0.1387 +0.19
0,8577
=043
0.9081
+0.21
0,2469
+0.57
0.3323 =0.33
ContraRec
0.1357
+0.62
0,2258
+0.58
0.0832
+0.27
0.1
+0.78
0.8275
+3.79
0.8760
+2.45
0,2043
+0.8
0,2980
+0.74
ReclMit
0,1460
21.00 0,2659
2050
0.0886
20.00
0,1269
#0.34
0.8424
20.76 0.9006
20.48
0,2166
=1.24
0,3343 =0.33
HDInt
0.1471 40.40
0,2654
=1.00
0.0852
#0.25
0.1230
#0.24
0.8306
=0.38
0.8881
#0.55
0,2168
#0.61
0,3301 #0.83
PRISM
0.1700 21.31
0,2339
a1.56
0.1181
10.62
0.1388
10.69
0.8398
23.91
0,8919
11.91
0,2418
11.39
0,3065 11.28
DreamRec
0,1819
20.84
0,2426
20.67
0.1313
10,60
0,1509
20.54
0,9002
+2.91
0,8952
13.05
0,2633
21.10
0,3176
20.87
DiffuRee
0.1402
+2.34
0,2095
+2.77
0.0919
+2.05
0.1142
+1.97
+3.18 0.8976
1.9114
0.2128
+2.57
0,2861
+2.49
PreferDifl'
0.1974
10.72
0,2620
50.59
0.1389
40..5
0.1598
#0.51
0.8693
#3.73
0.8874
+2.31
0,2713
20.85
0,3294 #0.30
Disco
0.2215
+0,89
0,2822 20,89
0.1580
+0.69
0.1778
#0.64
0.9305
#0,82
0.9264
#1.01
0.3000
#0.97
0.3507 #0.86
p-values
7.2c
6.0c ?
4.8c
6.6c
1.7c
4.3c
3.1c
1.5c
outperform traditional recommendation approaches. Specifically, Disco consistently achieves the best performance, while DiffuRec and PreferDiff generally rank second across most cases. This observation aligns with prior works (Yang et al. 2023b Liu et al. 2025a).
Other methods focusing credible content recommendation perform poorly under limited labeled data. Although methods such as Ree4Mit, HDInt, and PRISM aim to mitigate the recom- mendation of uncredible content, they assume full access to labels for all uncredible items. This assumption does not hold in real-world scenarios, where a large portion of content items remain unverified. Consequently, these methods cannot achieve satisfactory uncredible content mitigation, as they rely on accurate and complete labeled data to train classifiers for detecting uncredible items. This limitation motivates our design of the progressively enhanced credible subspace projection, which has been empirically shown to effectively mitigate uncredible content.
4.3 ABLATION STUDY AND HYPERPARAMETER ANALYSIS
Ablation Study. In this section, we evaluate the effectiveness of each key component of Di sco. We design six variants: (1) w/o Dis, which removes the disentanglement module (i.e., using origi- nal item embeddings for subsequent modeling); (2) w/o CSP. which removes the credible subspace projection; (3) w/o PERS, which removes the progressive enhancement of credible subspace; (4)

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Published as a conference paper at ICLR 2026
Table 2:
Ablation study of Disco.
Fact
GossipCop
MHMMsinto
Methods
HR@5
NDCG@5
CRes
HCes
HR@5
NDCG@5
CRØs
HC@5
HR@5
NDCG@s
CR@5
HC@5
D: 8.00
0,2664
0.1975
0.9835
0.3455
0.5236
0.399
0,9272
0.4918
0.2215
0.1580
0.9305
0.3000
w/o
0.2273
0.1605
0.9121
0.3033
0.4984
0.3678
0,478
0.1910
0.1342
0.8654
0.2650
w/o CSP
0,2575
0.1809
0.9431
0.3531
0,5183
0.3898
0.9155
0.4860
0.2178
0.1548
0.9066
0.2942
w/o PERS
0,2651
0.1919
0,9423
0.3393
0.5147
0.3891
0.9267
0.8766
0.2103
0,1498
0.9113
0.2877
w/o PC
0.2637
0.1934
0.9677
0.3413
0,4643
0.3531
0.9316
0.4651
0,2006
0,1419
0.8708
0,2747
wlo CE
0.1034
0.0622
0.7600
0.1626
0.0005
0.0003
0.7600
0.0010
0.1613
0.1011
0.8000
0.2299
w/ DDT
0.2609
0.1899
0,9499
0.3368
0.4025
0,3190
0,9069
0,4265
0.2089
0.1458
0.8466
0.2797
w/o PC, which removes the preference contrast term in Equation (5) w/o CE, which replaces cosine error with MSE in Equation [1 (6) w/ DDT, which utilizes disentangled embedding of tar- get item the diffusion target in Equation 4 As shown in Table 2 each component contributes positively. Specifically, removing the disentanglement module significantly harms model perfor- mance, highlighting that this module can effectively separate uncredible content and preserve users' preference-related information. FigureBJalso verifies that our designed disentangled diffusion model can effectively separate preference-reaate ible subspace projection and progressive enhancement of credible subspace significantly degrade Di Disco's recommendation eredibility (i.e., CR@5). In addition, without the preference contrast term, the recommendation accuracy will deteriorate. In particular, if not replacing MSE loss with cosine error, Disco's performance will be degraded to a great extent, due to unstable training (this issue is discussed in Appendix B.10 in detail). Meanwhile, if using the disentangled item embed- ding as the diffusion target, Disco cannot obtain satisfactory recommendation performance, due to ineffective disentanglement.
content and uncredible content. Both removing cred-
PolttFact
GossipCop
GossipCop
MiHMMisinfe
Ahhllamrics enisaddigg gange
Anaferanc urbedding
00033
10
0.48
0.29
0.8
0.82
0.27
0.31
0.26
0
1.5
0.5 1.5
05
15
2
30
19
W
W
Figure 3: Disentanglement visualization.
Figure 4: Effect of w on Disco.
Hyperparameter Analysis. The hyperparameter controls the contribution of negative preference diffusion. As shown in Figure 4 Di sco achieves the best performance when w = 0.5 on PolitiFact, = 1.5 on GossipCop, and w 1 on MHMisinfo. These results demonstrate the effectiveness of incorporating negative preference diffusion. However, continuously increasing the value of w leads to imbalance between different training objectives, thereby deteriorating model performance. More analysis of hyperparameters are provided in Appendix B.5] After analyzing the relationship between dataset statistics and the hyperparameter w, we found that the optimal value of w is pro- portional to the number of items in dataset. The hyperparameter w controls the contribution of the preference-contree
term, which involves sampling negative items from the set of un un-interacted items. When the number of items is larger, the sampled negative items represent a smaller portion of users' negative preferences. Therefore, a relatively larger us is needed to adequately learn users' negative preferences, thereby improving recommendation accuracy.
5 CONCLUSIONS AND LIMITATIONS
In this paper, we proposed Dis a model designed to steer DMs towards credible content rec- ommendation. To this end, we first designed a disentangled diffusion model to separate uncredible content from the generation process. Considering the limited labeled data, a progressively enhanced credible subspace projection is proposed to make the diffusion training process more credible. How- ever, similar to previous work (Liu et al. 2025a Yang et al. 2023b). Disco also requires a relatively large embedding dimension to achieve strong performance. This inevitably leads to increased train- ing time, which is common limitation of current DM-based recommendation methods (Liu et al.) 2025a). Future work could focus on designing DM-based models that maintain strong performance even under oiv-dimensional embeddings.
10

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Published as a conference paper at ICLR 2026
Ethics Statement. This paper aims to develop diffusion model (DM)-based method for credible content recommendation. The goal of our approach to serve societal good by mitigating the spread of uncredible content through recommender systems. We confirm that we do not anticipate any negative impacts and our work does not violate the ICLR code of ethics.
Reproducibility Statement. All results reported in this paper fully reproducible. The pseudo
codes of our model are provided in Algorithms | 2 and 3 The hyperparameter search space and experimental environment are discussed Section B.4 and Table 4 We provide the code and data of our method at
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A RELATED WORK
Our content recommendation task follows the paradigm of sequential recommendation (SRec) (Kang& & McAuley 2018: Wang et al., 2019a). Accordingly, our work is closely aligned with the re- search on sequential recommendation and diffusion model (DM)-based sequential recommendation. In this section, we review the studies on these two topics in detail.
A.1 SEQUENTIAL RECOMMENDATION
Sequential recommendation (SRec) has been widely studied in RSs Wang et al. (2023c), owing to the natural temporal order of users' behaviors (Wang et al. 2019a 202 2018 2019b Shi jt al. 2023). SRec can be technically divided into two categories: traditional sequential models and deep learning-based models. Traditional sequential models generally leverage sequential pattern mining (Yap et al., 2012) or Markov chain models (He & McAuley) (016) to model the item de- pendencies in users' interaction sequences. Traditional sequential methods can only capture simple interaction patterns or short-term dependencies, thereby cannot achieve satisfactory recommenda- tion performance. To overcome these limitations, deep learning- baaning-bbsed sequential recommendation methods are proposed to model complex and long-term dependencies in users' behaviors. Among this category, one research line focuses on designing the effective sequence encoders and backbone networks to encode users' interaction sequence, including GRU (Hidasi et al.
| (2015) CNNs (Tang) & Wang 2018). Transformer (Kang McAuley] 2018). and Mamba (Liu et al., 2025b). Building upon these, another research line further introduces advanced models, such Graph Neural Net- works (GNNs) (Chang et al.] []2T]] Caa et al. 2023 2022) and generative models (Deldjoo et al.
2024 Among them, generative models have recently attracted significant attention. In particular, DMs Liu et al. (2025a) and large language models (LLMs) (Sheng et al., 2025) have emerged as the most prominent approaches. DMM-ased methods will be discussed in detail in Section AA.2 With the widespread adoption of large language models (LLMs) across diverse application domains Yang et al. (2025) ; Li et al. (2025c); Zhang et al. (2024a), they have attracted considerable attention in RSs. LLM--based methods focus on leveraging the open-world knowledge encoded in LLMs to
enhance sequential recommendation performance (Harte al. 2023
A.2 DIFFUSION MODELS FOR SEQUENTIAL RECOMMENDATION
In recent years, owing to the strong capability to model complex distributions of user behaviors and item content, diffusion models (DMs) have been widely applied in recommendation scenarios (Wei & Fang 2025 Lin et al. 2024 Li et al. 2025a). including top-K recommendation (Wang et al. 2023d Zhao et al. 2024) and multimodal recommendation (Ma et el.) [024c] Li al. 2025a). In SRec, DM-based recommendation methods can be broadly categorized into two types: next item generation-based methods, and data augmentation- sequence encoders (e.g., GRU and Transformer) to encode users' context items into condition em- beddings, which then guide the generation of next items (Yang et al.] [2023b] Liu et al.] [2025a) Li et al. 2025b Cai al. 2025 Hu et al., 2024 Li et al. 2025b Ma al.) [024b Wang et al.) [024cc] Xie et af.] 2024). For example, (Yang et al. 2023b; Liu et al. (025a) utilize Transformer to learn condition embeddings from users' historical interactions, which are then utilized to guide the next- item generation process. The latter category leverages DMs to generate additional interaction data in order to enrich users' interaction sequences and alleviate sequence sparsity. For instance, (Liu et al. 2023 Ma et al. 2024a| Wu et al., 2023b) propose generating pseudo interaction sequences with DMs to mitigate the sequence sparsity problem. Additionally, several methods integrate con- trastive learning with diffusion models to generate augmented views, thereby enhancing the training
methods. The former generally employ
of DM-based recommendation methods (Cui et al. 2024b a Qu & Nobuhara 2025)
Although these methods have achieved remarkable success, they pose significant risk of generating uncredible content recommendations (e.g., fake news (Wang et al.) 2022bb 2024a Ma et al. 2025). misinformation (Pathak et al. 2023 Fernandez et al. (024)), which can severely harm both user experience and societal well-being. While (Ma et al. 2025) attempts to leverage DMs to mitigate fake news, its effectiveness is limited under the challenge of scarce labeled data. This limitation mo- tivates us to steer DMs towards credible content recommendation while simultancously addressing the challenge of learning from only limited annotated data.
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B MORE EXPERIMENTAL DETAILS
B.I DATASETS
In task setting, require users' chronological interaction sequences with content items, gether with labels indicating whether each item contains uncredible content. However, only lim- ited number of public datasets fulfill these requirements. this paper, we utilize three datasets: PolitiFact, GossipCop, and MHMisinfo.
The PolitiFact and CossipCop datasets are derived from the FakeNewsNet repository which col- lects data from two well-known fact-checking websites; PolitiFact and GossipCop. These datasets provide user-news interaction sequences along with labels that indicate whether each news article is fake or true. The MHMisinfo dataset is collected from a video-based mental health misinforma- tion datase containing users' interaction sequences with videos annotated by whether the videos contain mental health misinformation. Although this dataset records user-video interactions, the original video and image contents are not provided. Therefore, we represent the items using their video descriptions instead of visual features.
Given the high sparsity of these datasets, we adopt a data augmentation strategy following common practice (Yang al.) 2023b a). Specifically, for each user, transform their interaction sequence into multiple sub-sequences by treating each item as the target item and the items preceding it as historical context. This transformation increases the number of user-item interaction sequences and enriches the training data. The statistics of these datasets are reported in Table After augmenta- tion, the datasets have more sequences, thereby the recommendation performances of Rec4Mit and HDInt are different from the results reported in (Wang et al. 2022b) and (Wang et al. 2024a).
Table 3: The statistics of the three used datasets after preprocessing. PolitiFact
Datasets
GossipCop
MHMisinfo
Content items
616
9,529
3,160
Credible content items
306
6,792
2,815
# Uncredible content items
310
2,737
345
Training sequences
103,335
510,149
38,083
# Test sequences
21,490
68,002
8,060
B.2 BASELINE DESCRIPTIONS
In this section, we introduce the baseline methods used in our comparison.
Traditional sequential recommendation methods:
GRU4Rec (Hidasi et al. 2015) utilizes the Gated Recurrent Unit (GRU) to model the temporal dependencies of items in users' interaction sequences.
SASRec (Kang & McAAley 2018) employs the Transformer architecture to model the item de- pendencies users' interaction sequences. This one of the most representative sequential rec- ommendation methods.
Bert4Rec (Sun et al. 2019) replaces SASRec's unidirectional Transformer with a bidirectional Transformer architecture to model complex item dependencies. It also introduces cloze task paradigm for sequential recommendation.
LRU4Rec (Yue al. 2024) designs linear recurrent units for sequential recommendation. decomposes linear recurrence operations and proposes recursive parallelization, reducing model size and enabling efficient parallel training.
Contrastive learning-basee -ased sequential recommendation methods:
uses contrastive learning address the data sparsity problem in sequential recommendation. It designs three sequence augmentation operations for contrastive
CL4SRec (Xie
2022
Vttp:///gghaaacm/kKKalMM/////of hhnps://eemnoo.mn///ccm/////111
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learning:
item cropping, item masking, and item reordering. Transformer is used the sequential encoder CL4SRec.
ContraRec (Wang et al. 2023a) proposes two types of contrastive perspectives to enhance the per- formance of contrastive learning-base -hsed sequential recommendation: context-target contrast and context-contexi contrast. Transformer is used as the sequential encoder of ContraRec.
Sequential recommendation methods for mitigating uncredible content:
Rec4Mit (Wang et al., 2022b) first utilizes a disentangler to extract event- and veracity-aware information, respectively. Thereafter, the event embeddings utilized derive users' genuine preferences and predict the next items users may be interested in.
HDInt (Wagg et al. 2024a). Similar to Rec4Mit, HDInt is also dedicated to mitigating fake news in recommender systems. HDInt also considers the political bias. We omit this part, since requires additional data and the political bias is not considered in our task.
PRISM (Ma et al. 2025) proposes a protection-enhanced enhanced news recommendation method based on interest-aware sequential modeling. It utilizes DMs' controllable ability to learn user interest and mitigate fake news. However, it assumes all the labels of fake news are fully available, which does not hold the real world. is also a DM-based sequential recommendation method.
DM-based recommendation methods:
DeeamRec (ang al. 2023b) assumes that each user has "oracle" item in mind and selects items that match his ideal item. It uses Transformer to learn users' preferences, which then serve the condition for generating the oracle item for each user.
DeffuRec (Li et al. 2023) employs a diffusion model to represent item embeddings in a distribu- tion space and then feeds the embeddings into approximator to generate target item represen- tations. It argues that the standard objective function of DMs is unsuitable for recommendation tasks and uses cross- cooos-y loss optimize model parameters.
PifferDiff (Liu al. 2025a) proposes surrogate optimization objective which extend BPR recommendation loss (Rendle et al., (209)) to variational format. Meanwhile, this surrogate opti- mization objective can also extended multiple negative items.
B.3 EVALUATION METRICS
HR @ K and NDCG@K K are two commonly used metrics evaluate the recommendation accuracy, thereby we do not make further introduction for them. Credible Rate (CR@) K) is metric to measure the credibility a recommendation model. Specifically, it calculates the average rate of the credible content items in the recommendation lists:
K Rg n Ground truth
CR@K
unc
(13)
Stest
K
SESt
where Steat the set of sequences. R. is the recommendation list for sequence s. GGroundtrruth denotes the ground-truth set of uncredible items. IR, n neg Grrooddd-true calculates the number of uncredible items in the recommendation list. The higher value of CR@K means the better performance in delivering credible recommendations.
In addition, we test how our methods perform in terms of both accurate and credible recommenda- tions, we design combined metric HC@K K (i.e., combining HR @ K and @R@K). Formally, HC@K calculated follows:
x HR@K x (CRØK/2)
HCOK = HROK + (CR@K/2)
(14)
This combined metric is inspired by the Fl-score, which combines precision rate and recall rate. To note that, since the values of HR@K and CR@K are not on the same scale, we divide CR@K with a factor of 2 to rescale it into similar value level with @R@K. This adjustment ensures fair combination; otherwise, the metric with much smaller magnitude would disproportionately dominate the combined score.
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B.4 IMPLEMENTATION DETAILS
In this paper, we consider a more challenging and realistic scenario in which only a small proportion of uncredible items are verified. To simulate this setting, we randomly select 20% of the uncredible items with available labels during the training process, while the labels of the remaining items are treated as unknown. It is similar to the semi-supervised setting, In contrast, during the testing stage, all content labels are provided to enable an accurate evaluation.
The items in PolitiFact and GossipCop are news articles, and we use their textual descriptions as item content. In MHMisinfo, although the items are videos, only textual descriptions are available; thus, we can only rely on the textual descriptions for content representation. We encode these textual de- scriptions into language embeddings using LLaMA2-7B (Touvron et al. (023), and further project them into a lower dimension through an MLP. Following (Liu et al. 2025a), we fix the transformed embedding dimension at 3072 for all DM-based methods, as they exhibit strong performance only with higher embedding sizes. For other methods, the embedding size is set to 64. We also experi- mented with larger embedding sizes for these methods, but observed little and even performance drops for some methods, consistent with the findings in (Liu et al.) 2025a).
performance gain,
In implementation, we select Transformer as sequence encoder. Following the standard configuration (Vaswani et al., 2017). the Transformer architecture in our implementation includes multi-head attention, position- wise feed-forward network, layer normalization, and dropout.
For our method Di sco. the hyperparameter w is tuned within {0.5, 1, 1.5, 2, 5). We fix m at 10,000 and tune y within 10.1, 0.2, 0.3, 0.4, 0.5) to control the maximum selection ratio as well as the growth rate of the current selection ratio. The maximum number of diffusion steps is fixed at 2,000 and the DDIM step is set 100, following the settings of (Liu et al. 2025a For all DM-based methods, we utilize linear schedule for B, in range [0.0001, 0.02]. In our implementation, we do not use a classifier-fre free guidance (Ho & Salimans). since we found does not influence much the performance of D sco. In our implementation, we found that the singular values in A are relatively large; therefore, the threshold for constructing the null space of uncredible features is fixed at 3 for all datasets in our experiments. We search learning rate in range (1e-5, 5e-5, le-4, 5e-4, 1e-3). The batch size is searched in (2048 x 2' 2= The model parameters are initialized using normal initialization and optimized by AdamW (Loshchilov & Hutter, 2017). The hyperparameter settings of baseline methods are reported in Table 4 All experiments are conducted on an NVIDIA A40 GPU with 48 GB of memory. Each method run five times, and we report the average performance along with the standard deviation.
Table 4: The hyperparameter settings of baseline methods.
Methods
Hyperparameter searching space
GRU4Re:
In (le:..2,,5 3e-3, le.4), ). weight decay=0
SASRec
(o-21e- 5e-2 1o-3, 1o4), weight docay=0, mask probability 0.4, 0.6, 0.8)
Se-3, le-4), weight decay=0
Bert4Roe
LRURee
1e- 5e-3, le-1). weight decay=0), dropout (ute- 0.4, 0.6. 0.8}
CLASRee
Ir<:1o-2,,5e2.. (o-3, weight docay=0, mask/roorder/rrop propartion- (0.2, 0.4, 0.6. 0.8).
A <=-00.1..33....
ContruRee
Ir= 1e:3, 5e:3, weihtt decay=0, maskheommer/cropp propotion 0.4, 0.6, 0.8).
72 (.... 1]. (0, 0..000000000000 0.1, 1,
Rec4Mit
Ir=][1o-2,5ed 5e-2, 16-3, Io-4), weight decay=0, k (2,44. 20)
HDlnt
1o-2. Sc-2, 1o-3. 5o-3, 1o-4. weight docay=0. A 1. 72, 4, 10)
PRISM
le-flee2,50 1e:3, Se-3, le-4., weight decay=00, 7' > (500, 1000, 2000) w f0 2, 8).
Aor, Xe, Au, Aree (x(0.2. 0.6.
cmbedding size 258.256.621111 512, 1024, 2048. 3072) In (e-2. 5e-2, 3e-3, Se-3, Ie-4), weight decay=00 T /'0000000,000000 1000, 1500, 2000). 2,
DreamRec
embedding size-- 128, 256, 512, 1024, 2048, 3072)
DiffuRes
[l- 5e-2, le-3, Se-3, Io-4), weight decay=0, T ~(16, 32, 64, 128). 6-0.001.
embedding sizo-~(64, 128, 256, 1024, 2048, 3072)
PreferDiff
(1-2 Sc-2, 1e-3, 5e-3, 1e-1). weight decay=0, T < (500. 1000, 1500, 2000). n, 6.
A (00.2 0.4, 0.6, 0.8). embedding size> (64, 128, 256, 512, 1024, 2048, 3072]
B.5 MORE HYPERPARAMETER EXPERIMENTS
The hyperparameter " controls the selection ratio of potential uncredible items. We evaluate the performance of Disco (using combined metric HC@5) under different values of in range {0.1, 0.2, 0.3, 0.4, 0.5). As shown in Figure 5 Disco achieves the best performance when fixing w = 0.1 on PolitiFact and GossipCop, and 0.4 MHMisinfo. Lower values prevent the model
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PolitiFact
GossipCop
MHMisinfo
0.348
0.348
0.490
0.300
HC@5
0.485
0.344
0.295
0.480
0.342
0.290
0.475
0.340
0.285
0.1
0.2
0.3
0.4
0.5
0.1 0.2
0.5
0.1
0.3
0.5
7
y
Figure 5: Effect of y on Disco.
from effectively capturing potentially uncredible items, while higher values may introduce excessive noise, both of which degrade model performance.
B.6 TIME EFFICIENCY ANALYSIS
We conduct experiments to evaluate the training and inference cost (in seconds) of our model D sco and four DM-based methods under the same batch size. As shown in Table [ the training cost of Disco is relatively higher than DreamRec and PreferDift, mainly due to our additional designs for credible content recommendation, including content disentanglement and credible subspace projec- tion. This is acceptable due to the higher recommendation accuracy and credibility of our proposed method. Our training cost is much lower than that of DiffuRec an PRISM. As for inference cost, our proposed method D sco demonstrates the highest efficiency. This is because we adopt DDIM (Song al. 202T) as our generation strategy, which is more efficient than the DDPM (Ho et al, 2020) paradigm employed by DreamRec, DiffuRec and PRISM. Even compared with PreferDiff, which also adopts DDIM, Disco also exhibits higher efficiency. is because we do not employ classifier-free guidance in implementation, since it has limited influence
model while in- curring additional time consumption. Although D sic requires disentangling item embeddings first in the inference stage, it's inference cost remains comparable PreferDiff GossipCop dataset, which contain large number of items.
Table 5: Time cost (s) of different models on PolitiFact, GossipCop, and MHMisinfo.
Datasets
Time cost (s)
DreamRec
DiffuRec
PRISM
PreferDiff
Disco
Training/epoch
9.4
74.8
25.3
11.3
12.6
PolitiFact
Inference
278.2
224.3
994.3
15.2
10,5
Training/epoch
43.3
376.9
137.6
58.8
73.1
GossipCop
Inference
956.8
783.1
3223.6
127.9
133.5
Training/epoch
3.3
26.9
8.9
3.9
4.2
MHMisinfo
Inference
107.6
87.1
372.9
8.7
7.7
B.7 DISCUSSION ON EMBEDDING DIMENSION
As pointed out in our paper, DM-based methods can only achieve strong performance when the embedding dimension high. demonstrate the necessity using high embedding dimension (i.e., 3072) for all DM-based methods, we conducted experiments DM-based methods when fixing embedding dimension to 64. The results are shown Table 6
As shown in Table 6 not only Disco but also other DM-based recommendation methods (PRISM, DreamRee, and PreferDiff) experience a substantial performance drop when the embedding size is reduced to 64. This observation is consistent with the findings reported in [1] and further validates the necessity of using high- high-dimensional embeddings in DM-based recommender systems.
Although higher embedding dimension increases the per-epoch training time, it also provides the benefit of significantly faster convergence, In our revised manuscript, we include a figure illustrating the convergence curves of Disco and SASRec. As presented in Figure 6 Disco converges much
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Table 6: Performance comparison under embedding dimension 64 for DM-based methods.
HR@5 HR@10 NDCG@5 NDCG@10 10 CR@5 CR@10 10 HC@5 HC@1 10
Methods
PolitiFact
GRU4Rec
0.2142
0.3390
0.1463
0.1863
0.9266
0.9122
0.2929
0.3889
SASRec
0.2158
0,3519
0,1386
0,1823
0,9059
0.9028
0.2923
0,3955
Bert4Ree
0.2191
0.3473
0.1472
0,1883
0,9127
0.9045
0.2960
0.3929
CL4SRec
0.2247
0.3527
0.1508
0.1919
0.9132
0.9027
0.3012
0.3960
PRISM (emh_size=3072)
0.1927
0.2758
0.1348
0.1615
0,9325
0.9178
0.2727
0.3446
PRISM (emh 6ize=
0.0806
0.126)
0.0569
0.0715
0.7807
0.7222
0.1336
0.1869
DreamRec (emb.size=307)
0.2416
0.3287
0.1767
0.2047
0.8620
0.8437
0.3054
0.3661
DreamRec (emb_size=64)
0.0814
0.1074
0.0651
0.0734
0.5744
0.5605
0.1268
0.1553
PreferDiff (emb_size=3072)
0.2531
0.3554
0.1818
0.2147
0.8925
0.8981
0.3228
0.3968
PreferDiff (emb_size=644)
0.1227
0.1841
0.0882
0.1078
0.8934
0.8788
0.1925
0.2595
Disco (emb_size=3072)
0.2678
0.3775
0.1983
0.2336
0,9823
0.9425
0.3466
0.4192
Disco (emb (emb_size=64)
0.1171
0.1962
0.1107
0.1422
0.9916
0.9665
0.1895
0.2791
GossipCop
GRU4Ree
0.2226
0.3194
0.1466
0.1778
0.8864
0.8706
0.2957
0.3678
SASRec
0.3078
0.4706
0.1607
0.2135
0.8743
0.8526
0.3612
0.4473
Bert4Ree
0,2372
0.3711
0.1338
0.1770
0.8764
0.8587
0.3078
0.3981
CL4SRec
0.2898
0.4100
0.1784
0.2174
0.8938
0.8932
0.3516
0.4275
PRISM (emb sizc=3072)
0,2948
0.3447
0.2301
0,2463
0.8806
0.8733
0.3531
0.3852
PRISM (emb size=64)
0.0023
0.0034
0.0015
0.0018
0.6570
0.6940
0.0046
0.0067
DreamRec (emb size=3072)
0.4619
0.5501
0.3415
0.3704
0.8464
0.8336
0.4415
0.4742
DreamRec (emb_size=64)
0.0036
0.0049
0.0027
0.0031
0,5791
0.5903
0.0071
0.0096
PreferDiff min__szc=)377)
0.4969
0.6022
0.3655
0.3999
0.8307
0.8228
0.4523
0.4887
PreferDiff (emb sizc=64)
0.0084
0.0139
0.0053
0.0070
0.6542
0.7400
0.0164
0.0268
Disco (emb sizc=3072)
0.5236
0.6143
0.3996
0.4292
0.9272
0.9039
0.4918
0.5207
Disco (emb size=64)
0.0087
0.0162
0.0053
0.0077
0.7993
0.7993
0.0170
0.0311
MIMMisinfo
GRU4Rec
0.1151
0.1894
0.0760
0.0998
0.8380
0.8608
0.1803
0.2624
SASRec
0,1485
0,2592
0.0826
0.1179
0.8339
0.8915
0.2190
0,3276
Bert4Rec
0.1391
0.2299
0.0847
0.1138
0.8162
0,8786
0,2074
0.3017
CL4SRec
0.1734
0.2621
0.1101
0.1387
0.8577
0.9081
0.2469
0.3323
PRISM (emb ccmb_size=3072)
0.1700
0.2339
0.1181
0.1388
0.8398
0.8919
0,2418
0.3065
PRISM (emb-size=64)
0.0239
0.0295
0.0190
0.0208
0.7095
0.7317
0.0448
0.0546
DreamRec (emb_size=3072)
0.1819
0.2426
0.1313
0.1509
0,9002
0,8952
0.2633
0,3176
DreamRee (emb_sizc=64)
0.0282
0.0347
0.0233
0.0254
0.8281
0.8901
0.0528
0.0644
PreferDiff ccmb_size=307))
0.1974
0.2620
0.1389
0.1598
0.8693
0.8874
0.2713
0.3294
PreferDiff (emb. )iib_s)
0.0325
0,0380
0.0290
0,0308
0.8290
0.8989
0.0603
0.0701
Disco (emb.size=3072
0.2215
0,2822
0.1580
0.1778
0.9305
0.9264
0.3000
0.3507
Disco (emb_size=64)
0.0123
0.0572
0.0074
0.0213
0.9968
0.9873
0.0240
0.1025
more rapidly than the non-DMM method SASRec (embedding size 64). Specifically, Disco reaches its best performance at approximately the 40-th epoch, whereas SASRec requires around 400 epochs. This fast convergence rate Disco partially offsets the additional computational cost introduced by high-dimensional embeddings.
In addition, we conducted further experiments to demonstrate that Disco can still achieve superior performance compared with -M--badedd methods when the embedding dimension is restricted to 64, as long as a minor modification is applied to the overall optimization objective. In particular, augment Disco's is loss function with additional Cross-Entrops term:
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0.36
0.32
SASRec
HR H1@10
Disco
0.28
0.24
0.20
100
200
300
400
500
epoch
Figure 6: The performance convergence curve of SASRec and Disco on PolitiFact dataset.
Table 7: Performance comparison of Disco (optimized by Lpisco-) ) and non-DM recommendation
methods PolitiFact, GossipCop, and MHMisinfoo when embedding dimension is to 64.
HR@5 HR 10 NDCG@5 NDCG@10 10 CR@5 CR@ @ 10 HC@5 HC@ 10
Methods
PolitiFact
GRU4Rec
0.2142
0.3390
0.1463
0.1863
0.9266
0.9122
0.2929
0.3889
SASRec
0,2158
0.3519
0.1386
0.1823
0.9059
0.9028
0.2923
0.3955
Bert4Rec
0.2191
0.3473
0.1472
0.1883
0.9127
0.9045
0.2960
0.3929
CLASRec
0.2247
0.3527
0.1508
0.1919
0.9132
0.9027
0.3012
0.3960
Disco (emb sizes44 Lassco
0.2335
0.3555
0.1642
0.2034
0.9316
0.9213
0.3111
0.4013
GossipCop
GRU4Rec
0.2226
0.3194
0.1466
0.1778
0.8864
0.8706
0.2957
0.3678
SASRec
0.3078
0.4706
0.1607
0.2135
0.8743
0.8526
0.3612
0.4473
Bert4Rec
0,2372
0.3711
0.1338
0.1770
0.8764
0.8587
0.3078
0.3981
CL4SRec
0.2898
0.4100
0.1784
0.2174
0.8938
0.8932
0.3516
0.4275
Disco (emb size=64, Lossco')
0.4250
0.5060
0.3320
0.3583
0.9151
0.9080
0.4407
0.4786
MHMisinfo
GRU4Rec
0,1151
0.1894
0.0760
0.0998
0.8380
0.8608
0.1803
0.2624
SASRec
0.1485
0.2592
0.0826
0.1179
0.8339
0.8915
0.2190
0.3276
Bert4Rec
0.1391
0.2299
0.0847
0.1138
0.8162
0.8786
0.2074
0.3017
CL4SRec
0.1734
0.2621
0.1101
0.1387
0.8577
0.9081
0.2469
0.3323
Disco (emb bimbsizee=64, Loiseo")
0.1856
0.2660
0.1214
0.1475
0.8669
0.9129
0.2599
0.3361
exp(fo(ē', c .t),),)) ΣlEr exp(feefpp fo(e cpre, -c.)
Coisco = Lpisco log
(15)
The added term encourages the diffusion network fo to align its predictions more closely to the target items than with other items. Using this enhanced loss function Lpiseo*. Disco can achieve better performance than non-DM based methods. The comparison results reported in Table 7
As shown in the Table 7 Disco can achieve better performance than non-DM recommendation methods with only minor adjustment to its overall loss. This improvement arises because adding a discriminative loss (i.c.. Cross Entropy loss) to the generative loss (Cnisea) can partially mitigate the dimensionality limitations inherent to diffusion models.
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However, this practice will transform purely generative model into discriminative Our work does not make such a compromise. Nevertheless, the results clearly suggest that our model retains strong potential to surpass non-DM-based recommenders even when operating with substantially reduced embedding dimensions. This highlights the potential of Di sicco to achieve an effective trade-off between efficiency and performance.
B.8 DISCUSSION ON DIFFUSION STEP
In this section, we conducted experiments to investigate whether Disco still strong performance when the diffusion step 7' much smaller. The experimental results are reported in Table
From Table we can have the following observations:
As the diffusion step T increases, the performance of our proposed model, Di sisco, im- proves.
Disco maintains superior performance compared with non-DM methods even at much smaller diffusion step (T=100 for PolitiFact and GossiCop, and 7=500 for MHMisinfo).
Table 8: Performance comparison of Disco and non-DM recommendation methods under different
diffusion steps T.
HR@S HR 10 NDCG@S NDCG@ 10 CR@S CR@10 10 HC@S HC@ 10
Methods
PolitiFact
GRU4Rec
0,2142
0.3390
0. 0.1463
0.1863
0.9266
0.9122
0.2929
0.3889
SASRec
0.2158
0.3519
0.1386
0.1823
0.9059
0.9028
0.2923
0.3955
Bert4Rec
0.2191
0.3473
0.1472
0.1883
0,9127
0.9045
0.2960
0.3929
CL4SRec
0.2247
0.3527
0.1508
0.1919
0.9132
0.9027
0.3012
0.3960
Disco (Diffusion step T=100)
0.2494
0.3724
0.1751
0.2146
0.9488
0.9352
0.3269
0.4146
Disco (Diffusion step T=200)
0.2602
0.3752
0.1842
0,2211
0.9427
0.9340
0.3353
0.4161
Disco (Diffusion step 7=500)
0,2591
0.3828
0.1811
0,2208
0.9434
0.9272
0,3345
0,4193
Disco (Diffusion step T=1000)
0.2525
0.3784
0.1752
0,2156
0.9488
0.9369
0.3296
0.4186
Disco (Diffusion step 2'=2000)
0.2678
0.3775
0.1983
0.2336
0.9823
0.9425
0.3466
0.4192
GossipCop
GRU4Rec
0.2226
0.3194
0,1466
0.1778
0.8864
0.8706
0.2957
0.3678
SASRec
0.3078
0.4706
0.1607
0.2135
0.8743
0.8526
0.3612
0.4473
Bert4Rec
0.2372
0.3711
0.1338
0.1770
0.8764
0.8587
0.3078
0.3981
CL4SRec
0.2898
0.4100
0.1784
0.2174
0.8938
0.8932
0.3516
0.4275
Di 8:00
(Diffusion step T=100)
0.4603
0.5479
0.3419
0.3705
0.9304
0.9252
0.4627
0.5016
Disco (Diffusion step 7=200)
0.4759
0.5659
0.3537
0,3831
0.9242
0.9199
0.4689
0.5075
Disco (Diffusion step 7=500)
0.4867
0.5798
0,3621
0,3925
0.9258
0.9169
0.4745
0.5120
Disco (Diffusion step 1=1000)
0.4936
0.5956
0.3651
0.3984
0.9202
0.9039
0.4763
0.5139
Disco (Diffusion step 7'20000
0.5236
0.6143
0.3996
0.4292
0.9272
0.9039
0.4918
0.5207
MILMisinfo
GRU4Rec
0.1151
0.1894
0.0760
0,0998
0,8380
0.8608
0.1803
0.2624
SASRec
0.1485
0.2592
0.0826
0.1179
0.8339
0.8915
0.2190
0.3276
Bert4Rec
0.1391
0.2299
0.0847
0.1138
0.8162
0.8786
0.2074
0.3017
CL4SRec
0.1734
0.2621
0.1101
0.1387
0.8577
0.9081
0.2469
0.3323
Disco (Diffusion step 1==100)
0.1378
0.1998
0.0914
0.1111
0.8526
0.8783
0.2083
0.2746
1)i siccoo
(Diffusion step T=200)
0.1393
0.2191
0.0921
0.1178
0.9161
0.9209
0.2136
0.2969
101 sco
(Diffusion step 7=500)
0.1819
0.2610
0.1299
0.1553
0.9076
0.9144
0.2597
0,3323
Disco (Diffusion step T=1000)
0.2144
0.2638
0.1547
0.1708
0.9379
0.9311
0.2943
0.3358
Disco (Diffusion step 7=2000)
0.2215
0.2822
0.1580
0.1778
0.9305
0.9264
0.3000
0.3507
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B.9 DISCUSSION ON VARIOUS RATIOS OF AVAILABLE CREDIBILITY LABELS
we conduct additional experiments under different credibility label ratio of uncredible content (i.e., 5%, 10%, 20%, 30%, 50%). The experimental results are reported in Table 9 have the following findings:
From the results
reported in the Table,
Finding 1: As the credibility label ratio increases, the recommendation credibility (CR) improves steadily. This is because a larger number of credibility labels enables the con- struction of content to be mitigated more effectively.
comprehensive and accurate credible subspace, allowing uncredible
The recommendation accuracy fluctuates only slightly within narrow range across different label ratios. This stability is attributed to our proposed disentangled diffusion model, which effectively mitigates uncredible content while preserving users' preference-related information, thereby maintaining high recommendation accuracy.
Finding 2:
Table 9; Performance comparison under different credibility label ratios.
HR @5 HR 10 NDCG@5 NDCG@N 10 CR@5 CR@ 10 HC@5 HC@ 10 PolitiFact
Label ratios
5%
0.2617
0.3869
0.1819
0.2222
0.9422
0.9279
0.3365
0.4219
10%
0.2541
0.3836
0,1768
0.2184
0.9476
0.9357
0.3308
0,4216
20%
0.2678
0.3775
0.1983
0.2336
0.9823
0.9425
0.3466
0.4192
30%
0,2704
0.3838
0.1980
0.2345
0.9829
0.9518
0.3489
0.4249
50%
0.2678
0.3832
0.1923
0,2294
0.9842
0.9486
0.3468
0.4239
GossipCop
5%
0.5179
0.6021
0,4003
0.4279
0.9261
0.8726
0.4889
0.5060
10%
0.5290
0.6115
0.4089
0.4359
0.9266
0.8764
0.4940
0.5105
20%
0.5236
0.6143
0,3996
0,4292
0.9272
0.9039
0.4918
0.5207
30%
0.5196
0.6141
0.3947
0.4255
0.9278
0.9101
0.4902
0.5227
50%
0.5151
0.6069
0.3953
0.4253
0.9284
0.9176
0.4883
0.5226
MHMisinfo
5%
0.2127
0.2762
0.1500
0.1705
0.9149
0.9114
0.2904
0.3439
10%
0.2112
0.2798
0.1506
0.1728
0.9217
0.9152
0.2897
0.3473
20%
0.2215
0.2822
0.1580
0.1778
0.9305
0.9264
0.3001
0.3507
30%
0.2207
0.2836
0.1551
0.1755
0.9303
0.9244
0.2994
0.3515
50%
0.2134
0.2715
0.1533
0.1721
0.9331
0.9279
0.2929
0.3425
0.30
0.00E+00
Loss value
-4, 14.00E+17
HC@5
0.15
-87
-1.20E+18-
20
40
60
80
100
50
100
150
200
eopch
epoch
Figure 7: The loss and performance (HC @:5) curves of variant w/o CE on PolitiFact dataset.
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B.10 DISCUSSION ON THE INSTABILITY OF ABLATION VARIANT W/O CE
In this section, we conduct an empirical study on the convergence of the ablation variant "w/o CE". As show in Figure 7 we observe that the variant w/o CE (i.e., not replacing the MSE error with cosine error) leads to extremely unstable training and performance. Specifically, the loss rapidly collapses to an extremely small value (around -1.2 10 and the performance (HR @5) exhibits severe fluctuations. These results verify the necessity of replacing the MSE error with the cosine error to ensure stable optimization.
C WHY DMS POSE DANGER OF GENERATING UNCREDIBLE CONTENT RECOMMENDATION?
In this section, we empirically and theoretically analyze why existing DM-based recommendation methods risk generating uncredible recommendations.
C.I EMPIRICAL FINDINGS
In DM-based recommendation methods, the condition and diffusion target are two critical factors. In this section, we conduct experiments to examine how they influence the recommendation cred- ibility of DM--ased methods. Specifically, we divide the training dataset into four subsets based on whether the context items the diffusion target (i.e., target items) contain uncredible content. We use /f to denote that context items or target items contain uncredible content, and to denote the opposite. After this dataset division, we train methods (DreamRec (Yang et al., 2023b) and PreferDiff (Liu et al.) 2025a)) on each subset. From the results reported in Table IO] can find that these two factors indeed affect the recommenda- tion credibility of DM-based methods. We refer to these two factors as uncredible condition and uncredible diffusion target.
representative DM-based recommendation
Uncredible condition. When controlling the diffusion target, if the context items contain uncredi- ble content that leads to an uncredible condition, the credibility metric CR 10 (i.e., credible Rate) decreases some extent for both DreamRec and PreferDiff across the PolitiFact and GossipCop datasets. This finding indicates that the uncredible condition is factor contributing to the risk of DMs generating uncredible recommendation results.
Uncredible diffusion target. When controlling the condition, the diffusion target an un- credible item (i.e., uncredible diffusion target), CR @ 10 drops to extremely low level. This further emphasizes that the uncredible diffusion target is another key contributing factor.
Apart from these two findings, we also observe that training with the complete datasets yields worse recommendation credibility compared to the subset where neither the condition the diffusion target contains uncredible items. This further validates that the uncredible condition and the uncred- ible diffusion target are indeed the key contributing factors that place DM-based recommendation methods at risk of generating uncredible recommendation results.
Moreover, although simply removing uncredible items from the datasets can improve recom- mendation credibility, it significantly deteriorates recommendation accuracy. This is because uncredible items may also reflect users' genuine preferences, thereby discarding them restricts the model's 's ability to accurately learn users' true interests. Therefore, it is crucial to design advanced models that can mitigate the recommendation of uncredible content while simultancously preserv- ing high recommendation accuracy. This is the motivation and research significance of our proposed model, D Disco.
C.2 THEORETICAL ANALYSIS
Proof: Uncredible condition can enhance DM's generation of uncredible results
The training of conditional DM is to maximize (c. [log (e,///c)), where e,, is the diffu- sion target (i.c., the last item in a user's 's interaction sequence) and c the condition. This training objective pushes the generation toward the real data distribution.
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Performance comparison of DreamRec and PreferDiff under different settings of uncredi- ble content items in condition and diffusion target on PolitiFact and GossipCop datasets. Best results are highlighted in bold.
Table 10:
Whether contain uncredible content items?
Politi
Gossip
Methods
conditiio
diffusion target
HR@ 10
NDCG@ to CRØ 10
HC@ 10
HR@ 1o
NDCG@ 10
CR@10
HC@ 10
Training with complete datasct
0.3287
0.2047
0.8437
0.366
0.5501
0.3704
0.8336
0,4742
0.2674
0.1571
0,9935
0.3472
0.4658
0.3160
0,9771
0.4769
DreamRce
0.0577
0.0372
0.0522
0.2671
0.1541
0.9875
0.3467
0.1927
0.1368
0.9340
0.2728
0,0684
0.0413
0.0806
0.0507
0.0539
0.0404
0.0450
0.0317
Training with complete dataset
0.3554
0.2147
0.8981
0.3968
0.6022
0.3999
0.8228
0.4887
X
0.3035
0.1915
0.9591
0.3717
0.5036
0.3657
0.9315
0.4839
PreferDin
0.0587
0,0410
0,1073
0.0547
0.0407
0,0304
0,0833
0,0412
0.2625
0.1553
0.8561
0.3254
0.2074
0,1454
0.9076
0.2847
0.0568
0.0385
0.0837
0.0482
0.0573
0.0421
0.0254
0.0208
When an uncredible content-related condition e is utilized to guide the generation process, the model aims to maximize E peata (e,, (enn)) [log Po (enle"mo)]. Then, we have:
Pastt (c., cune) [log po (e,, c
=E -Epsata
Pasau (cune) BaaneyEpaaa Pupsata (e,,, cune) [log pe(en/ccng)
E (ounc)
Pdaata (ea (alcem))log po je
= Eppara (cune)
H1 (paata (e") ",
(16)
E Paato (cune)
H (pdata (c, [e)))))]
Epeara (cune)
[DKL (Piaata (er ( [po(e
= Hp (Eleune)
(eseca (Pdata (e
( |po(e (e
where & represents the whole generation space and e,, E E. Cune is the whole space of uncredible
condition cane (.,;) .) is the cross entropy between two variables or distributions. According the above derivation, we have:
H Hpanc (E(cune)=
E Epsera (o., oune) [lgg po(c,/cune))
E Epssia (esne) [DLL (Pdata (e, cliie) |po (o())))))))
(17)
S -Epaat (mne) [log (o
Ideally, when the model is optimally trained, the DKL term will approach zero, indicating that the conditional generation distribution approaches the real data distribution. Therefore, the mutual in- formation between the whole conditional generation space & and the whole uncredible condition space (une can be calculated as:
Ipo (E,Cune) -Ipaute (E,CuRC)
=H Hpscto (E) Ho (ElCun))
(18)
> Hpanto (E) + Expata (en (en,cuney [log pa po(en
The second equation is derived according to the property mutual information: 1(X, Y) = H (X) - H (X)). As the training goes the second term becomes larger. At the same time, Hpsero (E) is constant based on the real data distribution Pdota- Hence, the lower bound of Ipo (E, (un) also becomes larger. Based this, can conclude that training the diffusion model with uncredible conditions increases the mutual information between the generation space and the uncredible condition space. This indicates that the generation space increasingly contains uncredible features reflected the uncredible conditions.
Proof: Uncredible diffusion target enhance DM's generation of uncredible results
The optimization loss of existing DM-based recommendation methods can be formulated as:
L
Ille fo c,, 112)
(19)
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When uncredible item embedding ej ( Iunc) is used as the diffusion target (i.e., uncredible diffusion target) during training, the diffusion loss encourages the prediction direction of the diffu- sion network fo move closer to ej. Specifically, the MSE distance between the diffusion target and the output of fo will be smaller, indicating higher similarity.
In the inference stage, the generation process of diffusion recommenders can be expressed as:
c Wifo fo c, W2c2 + uze, (O,
(20)
1-a; W22 = va.(1-ā--1) and W3 -V 1- (1 - oz). This generation process
where W1
1-a.
is performed step by step, and the final embedding e, taken as the generation result, which then serves the reference for item prediction and recommendations.
Let - denote the generated embedding at step t without using uncredible diffusion target dj during training. In such case, the parameters of the diffusion network denoted 0. Similarly, let e denote the generated embedding at step L - 1 with ej as the uncredible diffusion target during training. In this case, the diffusion parameters are denoted as Ó. We then calculate the difference in similarity between the normalized ej and the normalized generated embeddings ét-1 and e at step t 1 as follows:
A
sim(ej, 1/-1) sim(e,,, 2
[w) (Ja(é) c, So(e C, ()) + wz (é), e%,) + wale 6 ej wi c,, - fo(e t)) ej + w2 (é), e*,)) ej
(21)
Here, utilize the dot product to calculate the similarity. To avoid the interference from sampled noise, we use e' to denote the sample noise e in step L - 1, and use it for both generation processes control this variable.
When f = T, we have:
T-1
sim(e,, eT simee o T-
Wi (fg(é,, c, fole,, T))
T
/ + uz (e_ e _.j
W1 (fo(E", c.T) - fo(c".c,T)) e
+ U2 (e e.e
W1 (Fo(e ,, e -fo(e" .)TT)
(22)
wi (sim (e,, Sg(E" ,,,)) - sim (ej, Jo (ET, c, c,,T))) 00
We control the process of two generations start from the same point oØ¹ e = E for fair com- parison. As mentioned earlier, the prediction direction of ʃg is closer to ej than that of fo. Hence, the MSE distance between the output of ʃg and ej is smaller than that between the output of fo and ey. When the embeddings are normalized, a smaller MSE distance corresponds to higher dot product similarity. Consequently, sim fg("", ,,, T))
sim So (6" c, T)) > O. At the same time, W1 > O, therefore A T-1 0. This indicates that, when starting from the same initial point, the generation result step produced by model fg. which has been trained with an uncredible diffusion target, will be more similar to this uncredible diffusion target.
When 1, we have:
A 2-2 w1 (fo(cT c, T
fo C.T C.T--1) cJ + +wy (6T-1-CT-1) wi CT + W2 (sim(e) 6/ -1) sim(e,, oT -1)
1) -
-1 cc
1i C + T-1
(23) As mentioned before, when a uncredible item embedding ej is taken for training, the diffusion loss will encourage the prediction direction of fg closer to ey. At the same time, éT- -1 closer to ej. as compared to that of e -1 This further enforces fg(e"-1 c, T - 1) more similar to ej, than that of fo(eT-1, c, T 1). Hence, the first term is a positive constant, and we denote it by C+-
Similarly, when < T we have:
A T-3
wi C+ A T22 T-2
= wi Cr T- + W2 W2 C+
W2 T T-1)
(24)
wi CT 2-- Wi W2 C + w T-1
27

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Published as a conference paper at ICLR 2026
T-
w1 W T-3
wi CT- -3 Wy W1 C T-2
W w2 CT + T-1)
(25)
wi - 3 w u2 C +
w
w
0+
T
+ w. T-1
A
w/ Cf + ww +... 1
r- 0+ -- + w/ 7-1 1---1
T-1
Σ w 2
C w T- - A-
(26)
>0 sim (e,, (%) - sim sim( e%) 0.
According to above analysis, the final generated result é, using diffusion network fa is more similar with uncredible item embedding éj. as compared to the final generated result ex, using diffusion network Se- This indicates that when uncredible items are used the diffusion targets during training. the model tends to generate outputs that carry more uncredible features, i.e., embeddings that are more similar to uncredible items.
Algorithm Training of Di sco
1: Input: Training dataset Strain {co.. Cnegs S sume) |Staial available uncredible item s=
set Tynes trainable parameters θ total diffusion steps T, learning rate variance schedules
)
2: Output: Optimized parameters O.
3: F =Stack(f (fe )iEIune)
Construct uncredible feature matrix
4: repeat
5:
(on, Gneg: grme 8 gunc) Strain
D Sample training data Obtain preference-related condition Obtain uncredible content-related condition Progressive uncredible feature matrix enhancement Construct null space of uncredible feature matrix Credible subspace projection for diffusion target e,, Residual connection Sample diffusion step Add noise to the embedding of diffusion target Reg Vāyeney ney VI are Add noise the embedding of negative preference item Update parameters
6: opre = -Transformert ( (grre)
7:
canc Mean(s (
8:
Update F by Algorithm
3
9:
U1; U2), A, V SVD
10:
én =, U2U,
11:
én = (ēn + e,,//2
12:
I Uniform (1,
13:
σ = Vare + vI OrE
θ O v/VeCnisod (ên; eneg cre cunc .),
16: until convergence
return θ
Algorithm Inference of Disco
1: Input: Test dataset Stest = pre p |
trained diffusion network parameters E θ, total reverse steps T. DDIM steps T", variance schedules {arTT
2: Output: A recommendation list for each user/sequence.
3: gare Stest
> Sample test sequence
4: cre Transformee(s"rr)
Obtain preference-related condition
5: for = T' .T.....11 do
6:
1 [t'x (T/T)J
Calculate DDIM denoising step Start from Gaussian noise
7:
of ~ ~0
8:
c Vo (0--(110)))
fo c + vor(l-8e- I-ar
ch +
- (1 at)
1-0:
Step-by-stey generation
9: end for
10: Ú: = e. e. > Calculate matching score between a user/sequence and candidate item e; Select top K items with highest matching scores
11: R (i))
12: return R
28

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Published as a conference paper at ICLR 2026
Algorithm 3 Progressive enhancement of uncredible feature matrix
Input: Original uncredible feature matrix F, available uncredible item set Zuncs current iteration
j. maximum selection ratio 7, maximum iteration m to reach
2: Output: Updated uncredible feature matrix F.
3: UD(i) [Zaeel Everunc cos(c, el
> Calculate uncredible degree of items in I Iunc
4: ratio(j)) = min(7, 2
D Calculate the selection ratio at current iteration 5: select ||Z Iunel ratio(j)] items with highest uncredible degree Select potential uncredible items
6: Add potential uncredible items into Tune
b Extension of uncredible item set Enhancement of uncredible feature matrix
7: F = Stack((e;")) (e("))iETnne )iETune)
8: return
D DERIVATION OF EQUATION 5
In this section, we provide the derivation of Equation 5 For simplicity, we only need to derive the first term, since the derivation of the second term follows the same procedure. The detailed derivation is as follows:
(el D:T cPre)
p(e e (a e,, C II Pe (et-1le' core) <(e He g(ee, lein elh)
-Eg log
En
log
plo c Pole e core)
II Pa (ch- 10 c ctre)
Eg
log
(e II:s
gieh
*le e0 Ig(e', le
ale, (o%)
p(eTTc "" )pa (e ee c IT pa (et le c
Eg
log
g (c])
II q(en
po (o(e
IT Pa (eh le cPre) He g t- l eh)
Egg
log p(c ens crre) log
log
q(c (.)
Po (o(e
Ey
[log p(e l, cure)] - -E, log
I
po (er ( ee,, opre))
Σ Byy log
q(eh leh, el))
t>
Eg [log plc lo caro]] + DKL. (o(ca)c))l
reconstrccton term
prior matching torm
Ey [DeL (g(c) cp (ct- cpre))]
denoising mutching term
(27)
Equation 2 is derived through Bayes rule: q(c), / .ch)- = afe' lof le(e Equation 4 is q(e). (eef-+leg)
obtained since p(c (rre) p(c..) given c, NO(, I), which is independent with condition cre
DMs generally optimize the denoising matching term DKL. (g(e)_"/ef, el)lppo (ef- erre)) ) in- stead of the whole variational bound. Then, this denoising matching term can be derived into the optimization loss L Eag eare,
[,,(e, eb) (ooe,, erre ,)] by adding the condition
207
cere into pa (e,, 1) in (Ho et al. 2020
Similar with (Pathak et al. 2023). M,yee, eg) is defined as
29

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Published as a conference paper at ICLR 2026
Pathak et al. 2023):
My(e e%, var(1 âr-1)e² Var-i(1 as)e
(28)
1- at.
In our model, μo (c, c .)) is defined as: po(e ctre, t) var(1 - dz-1)e, De, + vä.-i(1 (ole cpre,t)
(29)
where fo c () is the predicted e, using the diffusion network fo. Then, the optimization term can be rewritten as:
- opre,t
Ug [g c/h - -uooee +)12
2
var ar- ar- 1)e, + vār-i(1 ax)e
-Bel ,emre,
20²
1 āz
27
0r 0g ā + var-i(1 -a) c .t)
(30)
1 āz
22
1
vay-1 a1)
Va at))
27
e
epre,t
(1)
- -ā
en
-
fol fole cpre ,t)
-Ree eare,
1
V -(())
lle8 So(e cpre 1
(t)
Ct
is generally omitted (Ho et al. 2020). Hence, the optimization loss of our preference-related condition guided generation can be rewritten as Cpe = Egg Eeg.erre, ,F ||ea fo(c", c t))l Similarly, the optimization loss of uncredible content-
In practice, the coefficient (0
V T()aa)) 1-6e
related condition guided generation is: Lune cune, |||e% - fo(e", c . Our Disco
model aims encourage the generation guided by preference-relatee condition and discourage the generation guided by uncredible content-related condition. To achieve this goal, the optimization objective is formulated as shown in Equation 55)
= Lpre Lunc = Eeg core,t i [lle - So(e c.re [0][ Egg, gane,e [102 So So auc 1) [
(31)
E THEORETICAL JUSTIFICATION OF CREDIBLE SUBSPACE PROJECTION
Our credible subspace projection is constructed using SVD. Applying SVD to the uncredible feature matrix F yields the eigenvector matrix U and the diagonal eigenvalue matrix A. U and can be expressed
[U:; U2] and A
0 A2 Correspondingly, can be expressed as
V V1; V2). All zero near-zero singular values are contained in Ag, and the corresponding eigenvectors are given by U2 and V2.
According to the principles of SVD, the following equation holds:
F U.A.A.VVV..
(32)
Since the matrix U obtained from the SVD is an orthogonal matrix, we have:
U.F U A =
(33)
30

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Published as a conference paper at ICLR 2026
Our credible diffusion target is derived through é, Cn U2U Hence, we have:
e F
en U2 U =
(34)
This indicates that the derived credible diffusion target én is orthogonal the uncredible feature matrix F. In summary, our proposed credible subspace projection effectively removes uncredible content from the diffusion targets by ensuring that the projected targets lie orthogonally to the un- credible content.
F
CASE STUDY
In this section, we conduct study using the GossipCop dataset to evaluate the effectiveness of Disco. The GossipCop dataset contains users' interaction sequences with news articles, including both news (i.e., credible items) and fake news (i.e., uncredible items). Specifically, the historical interaction sequences and recommendation lists for five users. The credible content items marked in green, while uncredible items are marked in red. In addition, to illustrate the semantic relevance between content items, we utilize the same background color to highlight content with similar or related topies. From Table
present
have the following observations:
Disco demonstrates strong capability in delivering credible recommendations. Specifically, al- though all these users have interacted with uncredible items their historical interaction se- quences, the recommendation lists generated by Disco contain uncredible content.
Disce is capable of mitigating uncredible content while still preserving high recommenda- tion accuracy. This is achieved by removing uncredible features while retaining users' genuine preference-related information. For example, taking User4 cally interacted with some news (including fake news) about the death celebrities (highlighted in yellow). Di sco effectively capture this user's genuine preference and recommend some content also in such topics. worth noting that User4 had interacted with fake news about the death of "Tom Petty", and Di recommends this user with credible news article about the same event. This plays an important role in countering misinformation, as it helps users correct false impressions formed through prior exposure to uncredible content.
example, this user had histori-
G USAGE CLAIM OF LARGE LANGUAGE MODELS
We only utilize ChatGPT for polishing the academic writing, with the prompt "Proofread the gram- mar and polish the writing of the given sentences".
31

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Credible: Published as a conference paper at ICLR 2026
Table 11: Five cases showeasing the historical interaction sequences and the recommendation lists of five users sampled from GossipCop dataset. Credible refers to credible content (i.e., true news) and Uncredible refers to uncredible content (i.e., fake news). In user sample, the texts marked by the same-color background refer to similar topics. "ground truth" means the corresponding recommended content items have been actually read by the user in the test set.
Userl
Historical
Credible:
Justin
Uncredible:
Nicole
Uncredinle:ennall
Uncredible:
sequence
Timberlake, Chris
Kidman, Keith Ur-
Jenner Shades Scott
Grammy winners
Staplcton
release
ban: Socrcts to Suc-
Disick Over Photo
2018: the complete
'Say
Something"
Relationship.
Richie
song. video.
and Ilis Kids.
Recommen-
Credible Latin
Credible: Wernstein Company Files for Bankruptcy and Re- vokes Nondisclosure Agreements.
Credinle: Oscars:
Credible:
Crodible
Iground
dations
GRAMMY Awards
The Complete Win-
Pop superstar Lady
truth):
TV News
Complete
Winners
ners List.
Gaga has officially landed her first Las Vegas residency.
Roundup: Netfix
List.
Reveals Fuller House
Season 4 Premiere
User2
Historicul sequence
Credible: 13 Nights Halloween 2017 Schodule: Full List of Movics.
Uncredible: Taylor Swift will reportedly
Uncredible: Former NBCC interviewer lashes out at Trump op-od for reportedly casting doubt on the authen- ticity infamous tupe
Credible: "Big Li-
Lies' Season News, Premiere Date & Cast.
keep her new album
off streaming ser- vices like Spotity and Apple Music for a
an
Recommen- dations
Credible
(ground
Crediille: Seven-time and defending cham- pion says she isa't quite realy return
Credihle:
Crodible: "Good morning baby of mine, John Sta- mos" fiance Caillio McHugh wrote as she debuted baby bump...
truth): Justin Tim-
Pop superstar Lady
Lyan Spears' second child on the way will join sister Maddie Briann.
berlake Announces
Gaga has officially landed her lirst Las Vegas residency.
New Album Man
the Woods.
after giving birth to daughter Septem- her.
listorical sequence
Credible: Hugh Grant and Anna cellation of the third Sex and City film came with headline- making falkout some thing Sarah Jessica Parker struggled with
Uncredible:
Uncredible: Selena Gomez: has com- plated her treatment for depression and anxiety and is re- ported feeling
Credible: Congratu- lations are in order Rachel McAdams 39-year-old ac- tress reporiedly he first-time mom! Though she personally
User3
Eberstein's baby on way their
daughter.
confirmed the baby news
Recommeen- dations
Credible: Chicago West Baby Photos Timeline.
All Credible: Demi Lovato Says She Contemllated Suicide at Age 7.
Credible: Black
Credible Iground truth): His wife
Credible: Tisha
Panther" is the most
Camphell-Mane Martin Files For Divorce
tweeted about movie ever.
Faith Hill said the country star had
From Husband of 21 Years
been suffering from dchydration.
Usee4
Historical soquence
Uncredible: Caitlyn Credible: Indiana Jenner told Diane police found the Sawyer that she actress unresponsive had undergone the after responding to final surgery in her gender resssignment procedlures on Friday night's s 20/20 special. Credible: An emo- tiomal Celine Diom returned the stage in Las Vegas on Tuesday night.
Credible: Roger Ailes. Former Fox News CEO, Dies Ai 77.
Uncredible:
Tom Petty Dead:
Celebrities
Reaci
Social Variety.
Media
911 call Saturday.
Recommen- dations
Credible truth): Rocker
(ground
Credible: Hugh Hefner'ss death cer- tificate from
Credible: The final season of Netllix's 's "House of Cards" keeps the sccret of how Frank Under- wood died until the very end.
Credible: Pauley Per-
relle announces she's leaving "NCIS" after 15 scasons.
Tom Petty died
Monday after being Los Angeles hospital.
Los Angeles County
rushed
Department Public Health.
Historical sequence
Credible: Benjamin Glaze had never kissed girl before
Uncredible: During her chat with Ryan Murphy
Credible: A longtime acrialist for the famed Cirque Soleil plummeted to his death in front of horrified crowd in Florida Saturday night while trying out new act...
Uncredible: Justin Bieber's struggling with his split from
User5
Friday (March 16) for the opening night PaleyFist in Los Angeles.
Katy Perry tricked
Sclena Gomez as she's all amiles her Australian vaca- tion.
him during ABC
reboot of American
the Biehs is coping with his..
Recommen- dations
Credible (ground truth): Justin Bicher
Credibbee The singer covered
Credihle: Thevoroww helmed the rebooted Grande's 'Just franchise's first stallment.
Credible: Vnting closes at 5pm PT today (June 29) for this year's News' TV Scoop Awards...
Credible:
Blkk
Ariana
Shelton Gets
His
Wants With
Palms Read With
Selena Gomez But
Litle Bit of Your the arena where her concert was attacked...
Jimmy Fallon, Jakes
Is Hanging With
Ahout Having Too
Baskin Champion.
Much Sex.
32


Reviews:
[{"review_id": "WCg6eehbWD", "reviewer": "Reviewer_o9ud", "summary": "This paper proposes Disco, a diffusion model-based recommendation model designed to mitigate uncredible content while maintaining recommendation accuracy. It identifies two key factors that cause existing diffusion models to generate uncredible recommendations: (1) uncredible conditions when users have interacted with uncredible items, and (2) uncredible diffusion targets when the target item itself is uncredible. Disco addresses these through a disentangled diffusion model that separates preference-related and uncredible content signals, combined with a progressively enhanced credible subspace projection that projects diffusion targets into the null space of uncredible content features.", "questions": "1. If Disco uses a low-dimensional embedding (e.g., 64 or 128) or fewer time steps, how does it perform comparable to the non-DM baselines?\n2. How does the method perform when the proportion of labeled uncredible items varies (not just 20%)?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Credible content recommendation is a new but important problem. The paper addresses a significant real-world issue - recommender systems amplifying misinformation and fake news.\n2. The paper considers a realistic scenario where only 20% of uncredible items are labeled during training, which can be more representative than full label availability.", "weaknesses": "1. The strong performance relies heavily on comparing against baselines (Traditional, Contrastive, and generic DM-based methods) that were not originally designed to address credible content recommendation under conditions of limited labels.\n2. Achieving strong performance requires a large embedding dimension (e.g., 3072 for DM-based methods) and a large number of diffusion steps. How to deal with the tradeoff between efficiency and performance is a challenge.\n3. The overall framework is intricate, integrating a disentangled diffusion model, a separate credible subspace projection, and a preference contrast term. The paper does not well explore the intrinsic connections or necessity of coupling all these components, potentially make the design of the model a bit too complex.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zfuVtAgi8A", "reviewer": "Reviewer_5evV", "summary": "The paper proposes Disco, a novel framework aimed at steering diffusion models (DMs) towards credible content recommendations. While DMs have proven effective in recommendation systems, they risk generating uncredible content such as fake news or misinformation. Disco addresses this issue by introducing a disentangled diffusion model that separates credible user preferences from uncredible content. This separation ensures that content generation is guided by preferences while discouraging the inclusion of uncredible signals. Additionally, Disco incorporates a progressively enhanced credible subspace projection, which suppresses uncredible content by projecting the diffusion targets into a null space that excludes uncredible features. The effectiveness of Disco is demonstrated through experiments on real-world datasets, showing that it delivers both accurate and credible recommendations.", "questions": "See weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Innovative Approach to Credibility: Disco offers a pioneering solution to mitigate uncredible content generation in DMs, which is crucial for real-world recommendation systems, particularly in sensitive areas like news recommendations.\n\n2. Disentanglement of Credible and Uncredible Content: The disentangled diffusion model efficiently separates preference-related content from uncredible content, preserving the user’s genuine preferences while filtering out harmful signals.", "weaknesses": "1. **Paper Writing**: The formatting in Section 2.2 is quite poor, which makes it difficult to follow the explanation. Furthermore, the terms preference-aware embedding and uncredible content-aware embedding are introduced, but their definitions and distinctions aren't clear until Section 3.1. This lack of clarity is problematic, especially in the context of recommendation tasks or datasets, as readers are left unsure about what these embeddings represent and how they differ from each other until much later in the paper. Providing clearer definitions and explanations earlier in the paper would greatly improve the readability and understanding of these concepts.\n\n2. **Novelty**: The Credible Subspace Projection seems to be inspired by AlphaFuse and other heuristic methods. The formulation in Eq (4), where the two conditions are directly subtracted, also appears heuristic. It's worth questioning why this approach of direct subtraction was chosen—why not explore other possible forms?\n\n3. **Experiments**: All the experiments are conducted on datasets related to Fake News or misinformation videos. Has the author tried applying the method to other domains, such as Beauty or Sports? Expanding the dataset to include more diverse domains would help verify the method's effectiveness and generalizability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lIlyRItS8T", "reviewer": "Reviewer_Vtvf", "summary": "This work primarily addresses the critical problem of untrustworthy content generation in recommendation systems. To mitigate this issue, the authors propose Disco, a framework designed to guide diffusion models toward generating credible content recommendations. The core idea is to reformulate the diffusion objective by incorporating preference-related signals that encourage credible generation, while simultaneously suppressing signals associated with untrustworthy or low-credibility content. Extensive experiments conducted on three benchmark datasets demonstrate the effectiveness of Disco.", "questions": "1.  Authors should explain how the Disco framework identifies uncredible content or ensures the generation of credible recommendations. The authors should further elaborate on (1) how credibility is defined and operationalized within their framework, (2) whether the model explicitly detects or filters uncredible content, and (3) which metrics or benchmarks are used to quantify credibility in the evaluation. \n2. The authors should provide empirical evidence to support the claim that ID-based embeddings are capable of identifying uncredible content. Without such validation, it remains unclear whether these embeddings capture meaningful credibility-related signals. Additionally, a visualization demonstrating the consistency between the learned embeddings and the ground-truth credibility labels would help substantiate this claim. \n3. The proposed improvement to the diffusion model appears to be incremental, primarily involving the addition of a disentanglement strategy. The authors should provide a more detailed comparison with existing diffusion-based recommendation methods such as DreamRec and DiffuRec. \n4. The motivation behind the use of null-space projection for filtering uncredible information is insufficiently explained and appears debatable. \n5. The authors should consider larger and more diverse datasets to validate the model's effectiveness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The critical problem of untrustworthy content generation in recommendation systems is highly relevant to both the broader societal context and the ICLR research community, given its implications for information integrity, and user trust.\n2. The authors focus on addressing the limitations of diffusion models in content recommendation and propose an effective framework for credible content generation, which presents an interesting to the field.\n3. The authors provide sufficient experimental validation of the proposed Disco framework, including comparative evaluations, ablation studies, and hyperparameter analyses.\n4. The authors provide sufficient mathematical proofs to support the theoretical soundness of the proposed Disco framework.", "weaknesses": "1.  While this work aims to advance credible content recommendation, the experimental results primarily focus on traditional accuracy-based metrics, without providing dedicated evaluations or metrics to assess credibility. It remains unclear how the Disco framework identifies uncredible content or ensures the generation of credible recommendations. The authors should further elaborate on (1) how credibility is defined and operationalized within their framework, (2) whether the model explicitly detects or filters uncredible content, and (3) which metrics or benchmarks are used to quantify credibility in the evaluation. \n2. Fake news is often defined based on human judgment, which inherently involves subjective and context-dependent factors. However, the Disco framework relies primarily on ID-based embeddings to identify uncredible content, which may lack the semantic richness and contextual understanding necessary to capture the nuanced nature of credibility. Consequently, the motivation for improving content credibility by adjusting the diffusion process solely based on ID-level signals is potentially limited and requires further theoretical justification and empirical validation.\n3. The proposed improvement to the diffusion model appears to be incremental, primarily involving the addition of a disentanglement strategy. To better position the novelty of their work, the authors should provide a more detailed comparison with existing diffusion-based recommendation methods such as DreamRec and DiffuRec. Specifically, it is important to clarify how Disco differs in terms of model architecture, and objective formulation.\n4. The motivation behind the use of null-space projection for filtering uncredible information is insufficiently explained and appears debatable. While the method is intended to suppress untrustworthy signals, the theoretical justification for why null-space projection effectively filters such content is not clearly articulated. Moreover, the authors do not provide any dedicated experiments to validate the effectiveness of this mechanism in isolating or removing uncredible information. \n5. The three datasets used in the experiments are relatively small and may not be sufficient to robustly validate the effectiveness and generalizability of the proposed model. To strengthen the empirical evaluation, the authors are encouraged to consider larger and more diverse datasets that better reflect real-world recommendation scenarios.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gGwBwDN0VT", "reviewer": "Reviewer_fb2b", "summary": "This paper proposes Disco, a diffusion model-based recommender system designed to mitigate uncredible content recommendations such as fake news and misinformation. The authors identify two factors causing existing diffusion models to generate uncredible recommendations: uncredible conditions from users' historical interactions with uncredible items, and uncredible diffusion targets when the target item itself is uncredible. Disco addresses these through three main contributions: a disentangled diffusion model that separates preference signals from uncredible content signals by jointly encouraging generation conditioned on preference-related embeddings while discouraging generation conditioned on uncredible content embeddings; a credible subspace projection module using SVD-based null-space projection to remove uncredible features from diffusion targets; and a progressive enhancement strategy to handle limited labeled data by iteratively detecting and incorporating potential uncredible items. Experiments on PolitiFact, GossipCop, and MHMisinfo datasets with only 20% labeled uncredible items demonstrate improvements in both recommendation accuracy and credibility metrics.", "questions": "1. What characteristics of each dataset (e.g., proportion of uncredible items, interaction sparsity, content diversity) determine the optimal w? \n2. Can you provide guidelines or a principled approach for setting w on new datasets without exhaustive grid search? \n3. Given the data augmentation strategy that creates multiple sub-sequences from each user's interaction history (lines 772-776), how do you ensure no data leakage occurs when sub-sequences from the same user might span the train/test boundary?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. **Addresses a Critical Real-World Problem**. The paper tackles the important societal concern of recommender systems amplifying uncredible content, with well-motivated examples including COVID-19 misinformation spread (lines 59-62). The problem formulation is realistic, acknowledging that only partial credibility labels are available in practice (lines 135-138), which distinguishes this work from prior methods like Rec4Mit, HDInt, and PRISM that assume complete label availability. This practical constraint makes the research more applicable to real-world deployment scenarios.\n\n2. **Strong Theoretical Foundations for the design**. The disentangled diffusion model uses the diffusion objective itself as a disentangler without auxiliary networks (lines 211-214), reducing computational overhead. The reformulation in Equation 4 that minimizes variational bound conditioned on preference while maximizing it conditioned on uncredible content is theoretically well-motivated. The credible subspace projection using SVD null-space decomposition (Equations 7-8) is grounded in prior null-space projection work, and Appendix C provides both empirical evidence (Table 6) and theoretical proofs demonstrating how uncredible conditions and targets enhance uncredible generation, strengthening the paper's technical contributions.", "weaknesses": "1. **Limited Experimental Scope and Insufficient Baseline Analysis**. The evaluation is restricted to three datasets, all in news/video domains (lines 337-343), limiting generalizability to other content types like e-commerce or music. More critically, the paper fixes the labeled data ratio at 20% (line 360) without ablation studies on different ratios (e.g., 10%, 15%, 30%, 50%), making it unclear how sensitive Disco is to label availability.\n\n2. **Unclear Evaluation Protocol**. While the paper states \"complete labels are provided during testing\" (lines 137-138), it doesn't clarify whether uncredible items are included in the candidate pool during evaluation or how this impacts metrics like CR@K. The data augmentation strategy in Appendix B.1 (lines 772-776) transforms each user sequence into multiple sub-sequences, but doesn't specify how train/test splits prevent leakage when subsequences from the same user appear in both sets.\n\n3. **Inadequate Justification and Analysis of results**. Table 2's ablation reveals that replacing cosine loss with MSE (\"w/o CE\") causes severe performance degradation (HR@5 drops from 0.2664 to 0.1034 on PolitiFact, line 439), attributed to training instability (lines 227-229), yet the paper provides no learning curves, gradient analysis, or convergence studies to characterize this instability. The weight parameter w in Equation 11 varies across datasets (w=0.5 for PolitiFact, w=1.5 for GossipCop, w=1 for MHMisinfo in Figure 4), suggesting dataset-dependent tuning requirements that are not adequately discussed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)