Dynamic Open-book Prompt for Conversational Recommender System
Authors: Xuan Ma
Tieyun Qian
Ke Sun
Venue: EMNLP 2023 Findings (2023)
Keywords: Conversational Recommender System
Graph Learning
Prompt Learning

Abstract: Conversational Recommender System (CRS) aims to deliver personalized recommendations through interactive dialogues.
Recent advances in prompt learning have shed light on this task.
However, the performance of existing methods is confined by the limited context within ongoing conversations.
Moreover, these methods utilize training samples only for prompt parameter training.
The constructed prompt lacks the ability to refer to the training data during inference, which exacerbates the problem of limited context.
To solve this problem, we propose a novel Dynamic Open-book Prompt approach, where the open book stores users' experiences in historical data, and we dynamically construct the prompt to memorize the user's current utterance and selectively retrieve relevant contexts from the open book.
Specifically, we first build an item-recommendation graph from the open book and convolute on the graph to form a base prompt which contains more information besides the finite dialogue.
Then, we enhance the representation learning process of the prompt by tailoring similar contexts in the graph into the prompt to meet the user's current need. 
This ensures the prompt provides targeted suggestions that are both informed and contextually relevant.
Extensive experimental results on the ReDial dataset demonstrate the significant improvements achieved by our proposed model over the  state-of-the-art methods.
Our code and data are available at https://github.com/NLPWM-WHU/DOP.

Dynamic Open-book Prompt for Conversational Recommender System
Xuan Ma and Tieyun Qian and Ke Sun'
School of Computer Science, Wuhan University, China
Intellectual Computing Laboratory for Cultural Heritage, Wuhan University, China {yijunma0721 qty, sunke1 edu.
Abstract
Clierance 1
Conversational Recommender System (CRS) aims to deliver personalized recommendations through interactive dialogues. Recent ad- vances prompt learning have shed light on this task. However, the performance of exist- ing methods is confined by the limited con- text within ongoing conversations. Moreover, these methods utilize training samples only for prompt parameter training. The constructed prompt lacks the ability refer to the train- ing data during inference, which exacerbates the problem limited context. To solve this problem, we propose novel Dynamic Open- book Prompt approach, where the open book stores users' experiences in historical data, and we dynamically construct the prompt to mem- orize the user's current utterance and selec- tively retrieve relevant contexts from the open book. Specifically, we first build an item- recommendation graph from the open book and convolute the graph to form base prompt which contains more information be- sides the finite dialogue. Then, we enhance representation learning process of the prompt by tailoring similar contexts in the graph into the prompt meet the user's current need. This ensures the prompt provides targeted sug- gestions that are both informed and contex- tually relevant. Extensive experimental re- sults on the ReDial dataset demonstrate the significant improvements achieved by our pro- posed model over the state-of-the-at meth- ods. Our code and data are available at https://gtthub.com http://github.oomNLLLL. wow//o WWw.NNWWWW-WU/DOP.....
Jmm a litll down with kenuruw.
Got say movie ideass pick up my spiiiss
Movie watch
all time fave!
Unteranee peesent Recortens 75A
usovies please
Have you ever secn "The Cocjuning 2"!
RocomInzako
Twset to waich somching fimey.
Do you enjoy Adam Sandler movies? He's great in "50 First
Use 385
Recommadde
Figure I: A real example of the "open-book" recom- mendation process from the ReDial dataset.
involves two primary participants, i.e., the user sceking recommendations and the recommender providing personalized recommendations based on individual experiences. The recommender per- forms two closely related subtasks: the item ommendation subtask, which predicts the item that the user might be interested in based on the utter- ance context, and the response generation subtask, which generates human-like responses in reply to the user's previous utterance.
Early methods in CRS aim to ensure seman- tic consistency in two subtasks by employing various word-item alignment strategies, such as mutual information maximization (Zhou et al., 2020) and contrastive learning (Zhou et al., 2022). Recent advances in pre-trained language models (PLMs) (Gao et al., 2020; Min et al., 2021: Zhang et al., 2019; Li and Liang, 2021) have introduced new insights through prompt learning. This line of work is more data-efficient and flexible. Notably, a pioneering work (Wang et al., 2022) explores the generation the prompt by fusing item seman- tics from external data source and word semantics in an utterance, thereby enabling the transfer of knowledge from PLMs to CRS.
1 Introduction
Conversational Recommender System (CRS) (Jan- nach et al., 2021; Gao et al., 2021; Kang al., 2019; Zhou al., 2021) has emerged as a promis- ing approach for providing personalized recom- mendations to users through natural language utter- ances (Li et al., 2018a; Hayati et al., 2020). In con- trast to the traditional recommender system, CRS
Despite the remarkable progress in prompt learning-base based methods for CRS, the current
Corresponding author

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prompt design suffers from severe challenge, i.e. the limited context within ongoing conversations especially at the inference phase. the conven- tional training and testing scenario, the training data is solely used to learn prompt parameters and optimize the probabilities of ground-truw labels, with subsequent evaluation conducted on the test- ing data without extra information. However, it is hard for such prompt to memorize all training sam- ples and has the risk of forgetting them, thereby ex- acerbating the problem of limited context. Conse- quently, it hampers the generalization performance in finite dialogues during inference. This limita- tion can be analogized to studying from book but experiencing difficulties in recalling learned knowl- edge during a "closed-book"" examination (Meng et al., 2021; Chen al., 2022).
which memorize the current context and reflect the user's comprchensive needs. To prioritize the target user's needs when forming the prompt, we dynami- cally retrieve similar contexts from the open-book knowledge store by fine-tuning the prompt's 's repre- sentation learning process which consists of three strategies, i.e., enhancing the node embedding, en- hancing the graph structure, and passing the mes- sage. In this way, our model enables the prompt to provide informed and contextually relevant sug- gestions, thus guiding the PL.M generate the item-awedd wwem-aware representation. Extensive experimen- tal results on the ReDial dataset have shown the effectiveness of our proposed model.
2 Related Work
Conversational Recommender System Con- versational recommender systems have attracted attention due to recent advances in dialogue sys- tems. Li et al. (2018a) have made substantial con- tributions by introducing the ReDial dataset, a basic resource in this field. To bridge the semantic gap be- tween natural language expressions and users' pref- erences the item level, researchers have explored various types of external data sources (Chen et al., 2019; Ma al., 2020). Another research direction focuses on aligning diverse types of data (Zhou et al.. 2020; Speer et al., 2017).
To address this limitation, it is worth considering the analogical learning process observed in human cognition. Humans have the ability to retrieve rele- vant skills from deep memory through associative learning, enabling them to solve problems even in situations with limited contextual information. By leveraging the past successful recommendations as an "open-book" knowledge store, the recommender can learn and improve her recommendation strate- gies through process akin to copying from book rather than mere memorization. Furthermore, not all knowledge in the store is relevant current deci- sion. selective strategy necessary to effectively activate memory for current recommendation. For instance, in Figure 1, the recommender 758 has successfully suggested the movie "50 First Dates" to the user 903 and the movie "The Conjuring 2" to the user 752 based on their specific needs within the given context. However, when the user 285 arrives with preference for a funny movie, only the interaction with the user 903 can be referred to as she shares similar preference with the user 285.
Previous approaches to CRS have struggled with the integration of the recommendation and conver- sation module due to their disparate architectures. More recently, prompt learning has emerged promising solution fill this gap. For exam- ple, Wang al. (2022) leverage the knowledge- enhanced prompt based on a fixed pre-trained lan- guage model to fulfill both subtasks in a cohesive manner. However, the prompt in Wang et al. (2022) suffers from the limited context within ongoing conversations and has suboptimal performance. To address this issue, propose to construct dy- namic open-book prompt-based model which ex- plicitly creates an open-book knowledge store and retrieves relevant contexts as references from it, which can enhance the prompt's gencralization ca- pabilities during inference.
Motivated by these observations, we propose a novel dynamic open-book prompt method for CRS. On one hand, we aim to empower the prompt with historical experiences to enhance its contex- tual awareness within finite dialogues. On the other hand, we aim to tailor the prompt to fit the target user's needs in the current utterance. Specifically, our prompt aggregates the recommender's histori- cal accurate recommendations in the training phase via a global recommender-item graph, serving as an "open-book" knowledge store. Furthermore, we incorporate the local utterance-level semantics
Graph Learning Graph convolutional networks have found extensive applications across various tasks (Wu et al., 2020) by leveraging their ability to merge vertex features and input graph topology for efficient node embedding lcarning (Kipf and Welling, 2016). However, the construction the

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graph structure, typically performed manually or using the k-nearest neighbors method, pretty sus- ceptible to noise. Hence researchers propose to learn adaptive graphs for facilitating downstream graph-based tasks (Veličković et al., 2017; Li et al., 2018b; Fatemi et al., 2021). However, existing CRS methods still adopt a fixed graph which is unable to adapt to different conversations. To over- come this limitation, we propose to fine-tune the tiem-recommeenattion graph by enhancing the rec- ommender embedding, the graph structure, and the message passing process when constructing the prompt. Our enhancement allows for dynamic re- trieval of pertinent information from a static open- book knowledge store, satisfying diverse users' needs and evolving conversations.
3.1 Basic Open-book Prompt
The recommender plays a crucial role in two key subtasks: providing recommendations and respond- ing to the previous utterance. In the item recom- mendation subtask, the recommender's perception significantly influences the recommended items. Additionally, ensuring the coherence and consis- tency in dialogues paramount for the response generation subtask.
To achieve these, first leverage the recom- mender's representation the foundational prompt to guarantee the coherence in recommendation be- havior and response style for both subtasks.
We then leverage successful historical recom- mendations to build recommender-item graph G US where S represents the ver- tex set of |S| recommender nodes, denoted {si, $1s. and V represents the vertex set of [V] item nodes, denoted as {"1, 12, ..., "[v]]- The binary matrix R E I [|Ss describes the recommender-itee -tem interactions, where Rg if the recommender has correctly recommended the item and Rsv 0 otherwise. The graph G will serve as graph- reference book an open-book exam. During testing, the knowledge can be directly extracted from this "book" and used for accessing memory and enhancing the final rec- ommendation decisions.
Prompt Learning Prompt learning is a tech- nique that can enhance performance in various tasks by incorporating additional information, known as a prompt, into the generation process of pre-trained language models (PLMs) (Lester et al., 2021). Typically, the prompt is inserted at the be- ginning of the PLM's input to exert better control over its generation (Gao al., 2020; Radford et al., 2021; Jin al., 2021). Initially, the manual design of discrete prompts is a mainstream (Raffel et al., 2020; Brown et al., 2020; Petroni et al., 2019; Hsu et al., 2022). However, such prompts lack a fine- tuning process and require extensive experiments, experience, and expertise in language, incurring significant costs. Recent studics have paid more attention automatically optimizing continuous prompts (Gu al., 2021; Hu et al., 2021; Lester et al., 2021; Liu et al., 2022). We are also in line with this trend.
Finally, employ graph convolution to get the recommender's 's representation which forms basic open-book prompt. Through the integration of suc- cessful experiences stored in the open-book knowl- edge, the prompt gains the ability to directly extract contexts from the entire training data as the refer- ence. This enhances the prompt's 's understanding ca- pability and enables it to deliver more informed rec- ommendations to the target user, and consequently improves recommendation performance in finite dialogues during inference.
3 Methodology
In this section, present our proposed Dynamic Open-book Prompt (DOP) approach for CRS. We start by introducing the basic open- book prompt, which leverages users' experiences from training samples to enrich the current context and facili- tate decision-making even during inference. Next, we design three strategies to incorporate utterance- level semantics, which allow for dynamic retrieval in the "open-book" knowledge store to meet the specific needs of the target user. Following this, we finally describe how the prompt guides the PLM when performing the CRS subtasks. Figure 2 (a) illustrates a typical prompt learning framework for CRS.
3.2 Dynamic Open-book Prompt
The static "open-book" knowledge store for the prompt consists of past successful recommenda- tions catering to various users' needs. However, it may also contain irrelevant noises, which could potentially affect the performance when handling the target user's utterance. To address this issue, we propose three strategies integrate utterance-level semantics on the graph-based open-book knowl- edge store. By leveraging the utterance-Ievel se- mantics that memorize the current context and cap-

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Ougput
weight network that takes the utterance-level mantic representation с as input and generates a channel weight W to adapt the recommender's em- bedding. This weight network dynamically adjusts the importance of different channels the embed- ding process based on the specific characteristics of the utterance, formally described as:
Recoemmmner: sooemily saw The Big Sick (2077), whish good.
Prefrsincd language Model
Promut
Contest
Hello' Any good coneedy siggestios?...
(a) Prompt Leuming for CRS
Inforentee Minsry
Recormmedder nen Goph
Dher
lida
Azy goal corady saguntin
RoBERTa
W Reshupe W: (ReLU(Wie Wi b.)) + b2), (I)
Reuoeeoodd''s "Opaslook" Kanwletee Misiu
(2) Untermnn-evvi Seruntice Dheedieg
where W1 E Rkxd (k is the dimensionality for the hidden layer), W₂ E xf b E IR. and b2 E Rdd are weight matrices and biases. The op- cration Reshape refers to the dimension change: Rdd Rdxd The enhanced recommender's em- bedding 8 is obtained by multiplying the utterance- level semantic channel weight with the original embedding as: s Ws..
Strangiin for Echancinn Proupr
Woight Network 4-mm
af (1) boislly Edencca Enbekling
R
A
(2) Sehancing Stuctase
(3) Dxbnncng Moreat Pasnry troems
(b) Dynamic Open-book Prompt
The overall framework of our proposed model. Gray shapes indicate that the model parameters are fixed.
Figure 2:
Enhancing Structure to Form the Prompt By enhancing the graph structure the global recommender-item graph G and adjusting the ag- gregating weights of items, we can prioritize items that are contextually relevant to the user's utterance during the prompt's representation learning process. This enables us address target user's needs more effectively and improve the overall recommenda- tion quality. Specifically, for an utterance generated by the current recommender s; and an anonymous user, we calculate a new recommender-iten sim- ilarity matrix R' E R|S)x/V In this matrix, the entries in the i-th row represent the affinity based on the current utterance between the recommender 8; and all items in V, while the entries in other rows are to zero. To calculate each entry in the i-th row, we employ the following similarity measurement:
ture the user's comprehensive needs, we selectively retrieve relevant contexts from the open book while dynamically filtering out irrelevant information. The framework of our DOP is illustrated in Fig- ure 2 (b). We now proceed to elaborate each step in detail.
3.2.1 Utterance-level Semantics Encoding
We generate the utterance-level representation c by first feeding the utterance into the fixed pre- trained language model RoBERTa (Liu et al., 2019) and then encoding the output [CLS] vector of RoBERTa with a MLP layer.
3.2.2 Strategies for Enhancing Prompt
As mentioned earlier, we employ global recommender-item graph as the static "open-book" knowledge store for the prompt, and we wish to introduce utterance-level semantics for targeted rec- ommendations. To achieve this, we propose three strategies, i.e., enhancing the recommender's em- bedding, enhancing the graph structure, and pass- ing the message during the representation learning process of the prompt. These strategies guide the prompt's targeting process and enhance the infor- mativeness of the static "open-book" prompt.
< x W. v. Wa
RC al
).J (V.)...IVI)..
1
W. b W.
(2) where is the sigmoid function, W3 and Wa are two trainable matrices, <, denotes vector inner product operation, sy denotes the enhanced embed- ding for the recommender i, and v, denotes the embedding for the item j.
Besides, we only retain the entries with the top-k computed similarities in the i-th row to save the computational cost. Next, we combine the gener- ated matrix R' with the original matrix R in G. resulting in an enhanced graph structure R". This new matrix incorporates the contextual information from the current utterance and the recommender Sj, resulting in a targeted recommender-ing in- teraction matrix. We also introduce a constraint
Initially Enhanced Embedding for the Prompt By enhancing the quality of the recommender's embedding, we aim capture and comprehend the semantic features expressed in the user's utterance. This augmentation results in an improved represen- tation of the prompt, facilitating a deep understand- ing of the user's 's needs. Specifically, we introduce

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Joss term denoted as Leon, defined the Frobenius norm between R' and R, i.c., Leon = || R f ensuring the preservation of the original graph structure during this procedure.
to adapt the PLM for both CRS subtasks. For- mally, we prepend the prefix the input utterance sequence X' as follows,
X' = Xs.
(5)
Enhancing Message Passing Process to Form the Prompt By enhancing the message passing process, seamlessly incorporate the compre- hensive user's preferences, including likes and dis- likes expressed in the current utterance. This en- hancement empowers the prompt to directly cap- ture the associations between utterances and items, enabling it to adjust the understanding of user's preferences and aggregate neighborhood messages accordingly. With the enhanced graph structure R" recommender node embeddings and item node embeddings, propose an utterance-aware graph convolutional layer. Additionally, we introduce a filter component that interacts with contextual se- mantic features. This component plays crucial role in controlling the information flow during the message passing process, allowing for flexible re- weighting of the impacts between utterances and items when needed.
Subsequently, we provide the entire input X' to the PLM and calculate the decoder matrix in the last layer to facilitate learning for both the item rec- ommendation subtask and the response generation subtask.
Item Recommendation Subtask From the de- coder matrix, we extract the vector a for the last token in X' as the user's representation in the cur- rent utterance. Then, we compute the probability to recommend an item 2j for the user in the current utterance as:
P(av;) = soflmax(=" (vy)) uz).
We use the sum of the cross-entropy of each item's 's prediction and the ground truth as the rec- ommendation loss for the current utterance:
Lyvec Iy log P(æv;)+ + (1-1), 1,) (1 P(we;)) M),
(7)
Specifically, given the enhanced recommender's representation si and the item's representation Vi (v, -)). the output graph convolutional oper- ation at the 1-th layer is as follows,
where Yj denotes binary ground-truth label which is equal to 1 when the item ey is recommended for the user in the current utterance.
Finally, considering both the recommendation loss and the constraint loss, define a joint loss function for the recommendation subtask as fol- lows:
+ eWFe: (e) ① of
Σ
5
R%
k=
(3)
L Lyue a.
(8)
e dills
of
Σ (EM()y))
where a is the hyperparameter that balances the two loss terms.
VEM
R
Ry
(4)
where Ns;;) and (1;,) denote the neighbors of the recommender s; and item in the enhanced graph structure, and e denotes the utterance's 's em- bedding between the recommender s; and the target user during the conversation.
Response Generation Subtask Following the conventional setting of the response generation task, we employ the standard maximum likelihood estimator (MLE) as our optimization objective:
Legen
log P(un Jaq mex)),
Finally Enhanced Embedding the Prompt After the propagation process in the utterance- aware graph convolutional layer, we combine the outputs obtained at each layer to form the rep- resentation of the recommender 8; and item My: 8; Et= L; Uj El= LL, where s; is the prompt and vy is the item's representation.
where L is the length of the current utterance, Wkk is the k-th word in the utterance, wer denotes the words proceeding the k-th position and P(.) de- notes the distribution over the vocabulary for the next k-th word in the current utterance. Finally, the joint loss function combining Lgen and Loon for the response generation subtask is:
3.3 Training
lig = ligen + a.lcoon-
(10)
Inspired by PREFIX-TUNING (Li and Liang, 2021), we leverage the final representation of the recommender s; in the utterance as a general prefix
Note two subtasks in CRS are conducted separately, and we use the same hyperparameter a to reduce the overhead.

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4 Experiments
search in CRS, we evaluate the performance of all models on recommendation and conversation sub- tasks (Zhou et al., 2020; Wang et al., 2022; Zhou et al., 2020). For the item recommendation sub- task, we adopt Recall k (R @K) as our evaluation metric, where k takes on values of 1, 10, and 50. This metric measures the presence of ground-truth recommendations among the top-k items generated by the models. For the response generation subtask, we use the distinct n-gram metric with n values of 2, 3, and 4. This criterion assesses the response diversity which is a critical aspect for creating en- gaging and natural dialogue systems.
This section presents experimental results and the analysis.
4.1 Experimental Setup
Dataset We conduct experiments on ReDial (Li et al., 2018a), a real-world CRS dataset dedicated movie recommendation. It consists of 10,006 conversations, with a total of 182,150 utterances involving 51,699 movies. We split the data into training, validation, and test sets using an 8:1:1 ratio.
Setrings We implement models in PyTorch and train them on a NVIDIA 3090. The base PLM we use is DialoGPT-small (Zhang et al., 2019), which is pre-trained on 147M dialogues collected from Reddit, following UniCRS. Throughout the training process, the parameters of the base model remain fixed. The item embedding size and recom- mender embedding size are both set to 768, consis- tent with the input embedding size of DialoGPT- small. We employ 2 graph convolutional layers (L 2). During training, the batch size for the item recommendation subtask is 32, and for the re- sponse generation subtask, it is 8. For optimization, we employ the Adam optimizer (Kingma and Ba, 2014) with default parameter settings. The learning rate is set to 0.0005 for the item recommendation subtask and 0.0001 for the response generation subtask. The values of parameters i and a are de- termined based their optimal performance on the validation set, with & set 15 and set to 6. Baselines We compare our DOP with four groups of CRS approaches. (1) GPT-2 (Radford et al., 2019), DialoGPT (Zhang et al., 2019), and BART (Lewis et al., 2019) are pre-trained lan- guage models recognized for their effectiveness in language modeling and dialogue generation. (2)
4.2 Main Results
Table presents a comprehensive summary of the performance for the item recommendation subtask and the response generation subtask. Notably, our DOP model consistently outperforms other mod- els across all metrics, demonstrating its superiority. Based on these results, we draw the following ob- servations.
Table 1: The overall performance comparison on the ReDial dataset. The best performance among all in bold while the second best one is underlined. Numbers marked with an asterisk (*) signify statistically signifi- cant improvements compared to the best baseline (t-test with p-value < 0.01).
Recommendation
Conversation
Model
R@1
R@ 10 R@50 Dist-2 Dist-3 Dist-4
GPT-2
0.023
0.147
0.327
0.354
0.486
0..41
DialoGPT
0.030
0.173
0.361
0.476
0.559
0.486
BART
0.034
0.174
0.377
0.376
0.490
0.435
ReDial
0.023
0.129
0.287
0.225
0,236
0.235
KBRD
0.035
0.178
0.344
0.281
0.385
0.454
KGSF
0.035
0.179
0.369
0,311
0.443
0.545
C²-CRS
0.053
0.232
0.458
0.387
0.707
0.874
UniCRS
0.051
0.238
0.465
0.437
0.689
0.880
ReDial (Li et al., 2018a) is standard reference for evaluating the performance of our task. (3) KBRD (Chen et al., 2019), KGSF (Zhou et al., 2020), and C²-CSS (Zhou et al., 2022) are methods that incorporate various external data sources and employ dedicated word-item alignment strategies to bridge the semantic gap between the recommen- dation module and the conversation module. (4) UniCRS and BOP are prompt learning-based meth- ods. The former is proposed by Wang et al. (2022)
BOP
0.052
0.237
0.460
0.773
1,212
1.531
DOP
0.057"
0.257'
0.474"
0.876'
1.428"
1.827'
(1) The approaches that leverage more external data tend to achieve superior performance com- pared to other baselines. By incorporating addi- tional external data sources, approaches such as KBRD, KGSF, and C²-CRS enhance the semantic understanding of words and items mentioned in the dialogue, leading to a more accurate capture of user's preferences. For instance, KGSF lever- ages ConceptNet and DBpedia to enhance both word-level and leeel semantic information,
with external DBpedia data and the latter is our basic book prompt in Section 3.1.
Evaluation Metrics Consistent with prior re-

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surpassing KBRD, which solely incorporates the DBpedia data source.
Table 2: Results for ablation study on the item recom mendation subtask.
(2) A well-designed word-item fusion strat- egy effectively narrows the semantic gap between words and items, resulting in a more precise perfor- mance. For example, C2-CRS achieves a relatively high Recall @ score by employing a coarse-to co-se--t-finee --ne semantic fusion strategy, which combines different semantic spaces in a multi-graine grained manner.
Model
R@!
R@
R@50
DOP
0.057
0.257
0.474
DOP e/o-EM
0.050
0.243
0.456
DOP DOP w/a-LG
0.056
0.256
0.464
DOP 2//o MP
0.052
0.247
0.473
BOP
0.052
0.237
0.460
BOP wr/-O
0.048
0.218
0.428
best recommendation. (1) By removing the rec- ommender's embedding enhancement component, DOP w/o EM experiences the largest performance drop since the recommender's embedding func- tions as a final prompt to steer the PLM, which introduces the utterance-level semantics for effec- tively capturing the user's need. (2) The Recall @ 50 score for DOP w/o-LG LG decreases most, highlight- ing the significance of the enhanced graph struc- ture effectively emphasizing relevant items based on the utterance context within the prompt. How- ever, due to the expanded candidate pool in the prompt, achieving precision in specific recommen- dations like Recall @ becomes more challenging. (3) The simple graph convolutional layer setting DOP w/j MP without the utterance-itent interac- tion is inferior to the complete DOP. This implies that the utterance- -tem interaction can re- re-gh recommender-iten correlations and successfully capture the user's preference. (4) Comparing the results between BOP w/Pw//o- and BOP, we observe that incorporating the open-boot knowledge sig- nificantly improves the prompt. This integration leverages the training knowledge not only for prompt parameterization but also for direct refer- ence during testing, thus facilitating the recall relevant information.
(3) The prompt-based method UniCRS shows an overall better performance than many other base- lines. However, its prompt is enhanced by exter- nal data, which is not tailored for the user's need. contrast, our DOP approach focuses on user's demands. We employ utterance-level semantics to enhance the prompt's learning process and re- trieve relevant contexts in the graph-based open- book knowledge store. This enables direct access historical information and enhances the deci- sion quality. In addition, the performance compar- ison between BOP and DOP underscores the ef- fectiveness of dynamic strategies in integrating the utterance-level semantics. For the response genera- tion subtask, our DOP shows significant improve- ments in terms of diversity. This can be attributed to the inclusion of the recommenders' representa- tion, which encompasses their individual speaking styles and word choices. As result, our model gen- erates dialogues with each recommender's unique characteristics and augments the personalization beyond the scope of dialogue context.
4.3
Ablation Study
The merit of DOP lies in its dynamic prompt customization strategy, which utilizes three techniques to adjust the prompt within the graph- based "open-book" knowledge store based on se- mantics. We perform a series of ablation studies on each component of DOP to examine their impacts on the item recommendation subtask, including: (1) DOP w/ EM removes the recommender em- bedding enhancement component; (2) DOP w//o-LGG removes the graph structure enhancement compo- nent; (3) DOPw/o-MP removes the utterance-item interaction in the message passing process; (4) BOP further removes the open-book knowl- edge from the BOP.
4.4 Case Study
Table 3 presents two cases of the responses gener- ated by UniCRS and our DOP. We can observe that DOP model outperforms the UniCRS baseline in generating more informative responses.
In Case 1, we evaluate the model's ability to generate diverse initial recommender's sentences without context. Our model successfully gener- ates responses by leveraging the recommender's historical conversation information within the rec- ommender representation the base prompt. In contrast, UniCRS's prompt, which needs the on- going utterance context, is unavailable and thus ineffective. Consequently, our model is capable of generating responses that reflect the personal
As shown in Table 2, all variants suffer from a performance decrease. We can find that each component indeed contributes to the overall per- formance, and combining them together makes the

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Table Generation cases on the ReDial dataset.
4.5 Parameter Study
Label:
There are two key hyperparameters in our method: for the number of added edges and a to control the weight in the loss function. We only show the results of parameter study on the item recommenda- tion subtask due to the space limitation. In Figure 3, observe that the recall @ score and recall @ 10 score exhibit similar trend. Regarding the num- ber of candidate edges k, the performance initially improves as increases from to 15. However, it quickly deteriorates when & becomes larger. This suggests that suitable recommender-iten correla- tions in the prompt can effectively emphasize user's preferences, but too much information introduces noise. Similarly, the choice of the regularization coefficient is crucial for optimizing the overall objective. Excessive regularization hampers perfor- mance. In our experiments, we set k to 15 and to based on our analysis.
Recommender: Hi. I heard you were interested in finding some good movies. What kind of movies do you like?
Case
Generation:
UniCRS: Hi!
Ours: Hi. heard you might looking for a movie. What type of movies you like?
Context:
User: Hello! I want to go see movie in theater with my elderly grandma tomorrow. One she already saw and liked was A Wrinkle Time.
Recommender: Oh wow okay.
User: She likes drama and anything British that isn't It too racy.
Case 2
Recommender:
I think you need to see Ready Player Onc, Peter Rabbit, and Coco! Those would be great for you and her.
User: She might like Coco, but think Peter Rabbit bit too far out there for her.
Label:
Recommender: Sounds good! hope helped.
0.24
Generation:
UniCRS:
It is a good one.
Ours:
Perfect, think she would like
5.055
0.22
8.055
0.15
2.000
Recul 2013
Rccil
language habits of the recommender, as opposed to the generic "hi" produced by UniCRS.
0.000
0.25
0.000
Weighthitor
10
0.23
Figure Impacts of hyperparameters on the item recommendation subtask.
In Case 2, we verify the relevance of the dia- logues generated by our DOP model to the preced- ing context. By leveraging utterance-level seman- ties as prompt's guidance and selecting consistent knowledge from the open book, our model captures the user's specific needs and accurately identifies the role of "grandma", resulting in comprehensive and contextually relevant responses. On the con- trary, UniCRS soicly relies on external knowledge to enhance the prompt, overlooking the valuable information embedded in the utterance, thus failing consider the mentioned role within the context.
Conclusion
In this work, we propose an innovative approach to create a dynamic "open-book" prompt for the CRS task. Specifically, the open book stores histor- ical accurate recommendations as the users' expe- riences, and the dynamically constructed prompt memorizes the user's current utterance and selec- tively retrieves relevant contexts from the open book. Our objectives are twofold. Firstly, we aim to leverage the recommender's successful experi- ences to enrich the context besides the user's finite dialogue. This enables the prompt to refer the existing knowledge for docision-making during in- ference. Secondly, we aim to customize the prompt to match the target user's needs. Through three strategies to refine the representation Icarning pro- cess, our prompt can provide targeted suggestions that are both informed and contextually relevant. Extensive experiments conducted the ReDial dataset demonstrate the superiority of our proposed model over the state-of-the-a baselines.
In summary, our results prove that the diversity generated dialogues is critical for the integration of personalization and contextual information. Uni- CRS fails to leverage the relevant information from the recommender and lacks effective utilization of utterance-level dialogue context, leading to generic and repetitive responses. In contrast, our DOP not only integrates the recommender's representation but also employs dynamic strategies to explore the dialogue context. Thus, the generated responses are more targeted and diverse in nature.

---

Limitations
Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding. Yukuo Cen, Hongxia Yang, and Jie Tang. 2019. To- wards knowledge-based recommender dialog sys- tem. arXiv preprint arXiv:/9065 /908.0531.
We identify two primary limitations of this study that might be addressed in future research. The first limitation pertains to the model's inability to handle cold-start scenarios where the recommender lacks historical recommendations. In such cases, the recommender's representation lacks sufficient supervision, making it unsuitable as a base prompt. To address this, we consider leveraging the demand type from dialogue semanties as the base prompt to mitigate cold-start issues in the future. The sec- ond limitation is about the appropriate utilization of external knowledge. Although our model has achieved promising results, it does not leverage any external knowledge base. The judicious incorpora- tion of external knowledge to supplement semantic information is beneficial for natural language un- derstanding. In future work, we plan to enhance the comprehension of dialogue content by leverag- ing external knowledge repositories, such as the ConceptNet.
Xiang Chen, Lei Li, Ningyu Zhang, Xiaozhuan Liang, Shumin Deng. Chuanqi Tan, Fei Huang. Luo Si, and Huajun Chen. 2022. Decoupling knowledge from memorization: Retrieval-augmented prompt learn- ing, arXiv preprint 1.xXiv:2205.14700
Bahare Fatemi, Layla El Asri, and Seyed Mehran Kazemi. 2021. Slaps: Self-supervision improves structure learning for graph neural networks. Ad- vances in Neural Information Processing Systems, 34:22667-2288 22681.
Chongming Gao, Wenqiang Lei, Xiangnan Hc, Maarten Rijke, and Tat-Seng Chua. 2021. Ad- vances and challenges in conversational recom- mender systems: A survey, Al Open, 2:100-126.
Tianyu Gao, Adam Fisch, and Danqi Chen. 2020. Making pre-trained language models better few-shot learners. arXiv preprint wrXiv:2012.1572.
Yuxian Gu, Xu Han, Zhiyuan Liu, and Minlie Huang. Pre-trained prompt tuning for few-shot learning, arXiv preprint 1xxi:2110.04332
2021. Ppt:
Ethics Statement
Shirley Anugrah Hayati, Dongyeop Kang, Qingxi- aoyang Zhu, Weiyan Shi, and Zhou Yu. 2020. In- spired: Toward sociable recommendation dialog sys- tems. arXiv preprint arXiv:2009. 14306.
In this work, all authors acknowledge and adhere to the ACL Code Ethics, ensuring compliance with the code of conduct. This paper presents a method for constructing open-book prompt that dynam- ically integrates historical recommendations with the current user's demand for conversational rec- ommender system. For evaluation, we utilize the ReDial dataset, which consists of annotated dia- logues where people recommend movies to one another. The primary language is English. Our model neither introduces social/ethical bias nor am- plifies any bias present in the data.
I-Hung Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai- Wei Chang, and Nanyun Peng. 2022. Degree: A data-efficient generation-based event extraction model. In Pro- ceedings of the 2022 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1890-1908.
Shengding Hu, Ning Ding, Huadong Wang, Zhiyuan Liu, Jingang Wang, Juanzi Li, Wei Wu, and Maosong Sun. 2021. Knowledgeable prompt- tuming: Incorporating knowledge into prompt ver- balizer for text classification. arXiv preprint 1rXiv:2108.020::
Acknowledgments
This work was supported by the grant from the Na- tional Natural Science Foundation of China (NSFC) project (No. 62276193). It was also supported by the Joint Laboratory on Credit Science and Tech- nology of USCI-Wuhar University.
Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2021. A survey on conversational recom- mender systems. ACM Computing Surveys (CSUR). 54(5):1-36.
Woojeong Jin, Yu Cheng, Yelong Shen, Weizhu Chen, and Xiang Ren. 2021. A good prompt is worth millions of parameters? low-resource prompt-based learning for lssion-language models, arXiv preprint
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Reviews:
[{"review_id": "R2rJZEAs0n", "reviewer": "Reviewer_MYtF", "summary": "", "questions": "Can the authors explain the concept of an \"open-book prompt\" and the reason for using the term \"open-book\" to describe it?\n\nCould the authors explain why they chose RoBERTa in section 3.2.1 and selected DialoGPT as the base model for generation? Why were other pretrained language models not considered? Have the authors assessed the performance of other PLMs?\n\nThe recall@1 score for recommendation currently stands at approximately 5.7%, indicating a rather modest level of effectiveness. In light of this, is there a practical significance to employing such methods in real-world applications?", "limitations": "", "rating": 3, "confidence": 5, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1. The evaluation of the generation performance is questionable. The authors primarily present Dist-2, 3, and 4 scores in Table 1 as indicators of generation quality. Nevertheless, based on my personal experience, the generated sentences may exhibit considerable diversity (reflected in high Dist-n scores), yet still exhibit subpar readability and overall quality. Consequently, relying solely on dist-n scores may prove insufficient. To ensure a comprehensive comparison, it would be neccessary to incorporate additional generation metrics like BLEU alongside dist-n scores. Beyond automated metrics, it is imperative to complement the evaluation of generation performance with human assessments to substantiate the efficacy of the proposed method.\n\n**Response:** Thanks for your comments! We have taken your suggestions into serious consideration. Conversational recommender systems (CRS) aim to recommend high-quality items through natural language conversations. It consists of two subtasks: item recommendation and response generation. In the item recommendation subtask, our goal is to offer accurate recommendations to users. In the response generation subtask, our aim is to assist in the recommendation task with diverse responses. Therefore, the task emphasizes accuracy in item recommendation and seeks maximum diversity in response generation. Following previous CRS works such as UniCRS and KGSF, we adopt Distinct-𝑛 (𝑛=2,3,4) at the word level to evaluate the diversity of the generated responses. \n\nTo further verify the effectiveness of our model in terms of the generation quality, we have extended our evaluation metrics by incorporating the BLEU metric alongside the Distinct-𝑛 scores. \nWe present these additional metrics in Table I, comparing the performance of our proposed DOP model with the best-performing baseline, UniCRS. The experimental results clearly demonstrate our method maintains a strong performance in response generation accuracy, which proves that our DOP successfully captures the personal language habits of the recommender and integrates contextual information through the open-book prompt and is more effective for response generation.\n\n**Table I. Supplement results on the conversation task.**\n|           | Dist-2 | Dist-3 | Dist-4 | BLEU-2 | BLEU-3 | BLEU-4 |\n|:----------|-------:|-------:|-------:|-------:|-------:|-------:|\n| UniCRS    |  0.437 |  0.689 |  0.880 |  0.055 |  0.029 |  0.017 |\n| DOP       |  0.876 |  1.428 |  1.827 |  0.057 |  0.034 |  0.021 |\n\n\nMoreover, according to your suggestion, we have gone a step further by conducting human evaluations to complement the automated metrics. Following UniCRS, we invite three annotators to score the generated responses of our model and the best-performing baseline for 200 utterances from two aspects, namely Fluency and Informativeness. The range of scores is 0 to 2. Fluency measures how likely the generated text is produced by human. Informativeness measures whether the response provides new information and knowledge in addition to the post. The results are shown in the Table II below. As can be seen, the responses we generated are both fluent and rich in information. These results prove that the recommender’s experiences as an open-book store help the model effectively understand the dialogue history, and generate fluent and informative responses.\n\n**Table II. Supplement results of human evaluations.**\n|         | Fluency | Informativeness |\n|---------|-------:|---------------:|\n| UniCRS  |   1.39  |           0.97 |\n| DOP     |   1.41  |           1.04 |\n\n2. Can the authors explain the concept of an \"open-book prompt\" and the reason for using the term \"open-book\" to describe it?\n\n**Response:** In the \"Closed-book\" strategy, the training data is used solely to learn model parameters to maximize probabilities of ground-truth. Then, the model performance is evaluated based on the testing dataset without extra information. This is similar to how humans first acquire knowledge from books and then undergo a closed-book examination to recall the learned content. In fact, we are aware that the difficulty level of open-book exams is lower than that of closed-book exams because one can directly locate and copy relevant content from reference book. The term \"open-book prompt\" is inspired by the concept of human open-book exams. We believe that copying is simpler than relying on memory. If we can enable retrieval in an open-book scenario, it becomes possible to form prompts by retrieving recommendation scenarios relevant to the current dialogue's semantics from the entire training dataset. This approach enriches the prompt's information, allowing its direct utilization during the inference phase by referring to related knowledge in the training set. Therefore, we use the term \"open-book\" to indicate that our prompt possesses the ability to access open-book knowledge from the training phase and retrieve explicit information during the inference stage. \n\n3. Could the authors explain why they chose RoBERTa in section 3.2.1 and selected DialoGPT as the base model for generation? Why were other pretrained language models not considered? Have the authors assessed the performance of other PLMs?\n\n**Response:** UniCRS is the first work to develop a unified CRS in a general prompt learning way. This approach employs RoBERTa for encoding sentence semantics and utilizes DialoGPT for the base PLM since it has been pretrained on a large-scale dialogue corpus. In order to maintain experimental fairness and prevent potential performance improvements resulting from modifications to the pre-trained language model, our experimental setup mirrors that of UniCRS. Both RoBERTa and DialoGPT parameters are kept frozen during model training the same as UniCRS. \n\nWhile we acknowledge the significance of exploring a wide array of pretrained language models, our decision to align with the UniCRS framework was motivated by the desire to avoid inadvertent performance discrepancies that could arise from altering the underlying language model. Although we haven't explicitly evaluated other pretrained language models in this iteration of our study, we recognize this avenue as a promising area for future investigation.\n\nFurthermore, to demonstrate that our DOP does not incur the high computational cost, we compare it with the baseline UniCRS in terms of the parameter number and running time on two subtasks. From Table III below, we can conclude that our model does not add much cost and is relatively faster than the baseline. \n\n**Table III. Computational Cost Comparison.**\n| Model  | Recommendation    |                   | Conversation      |                    |\n|--------|-------------------|-------------------|-------------------|--------------------|\n|        | Parameter Number  | Runtime per Epoch | Parameter Number  | Runtime per Epoch  |\n| UniCRS | 585M              | 1402.8s           | 585M              | 2501.2s            |\n| DOP    | 531M              | 541.4s            | 531M              | 2132.5s            |\n\n4.\tThe recall@1 score for recommendation currently stands at approximately 5.7%, indicating a rather modest level of effectiveness. In light of this, is there a practical significance to employing such methods in real-world applications?\n\n**Response:** With the emergence of social chatbots, conversational recommender systems have gained prominence as a focal point, aiming to provide users with recommendation services through natural language interactions.\n\nFirstly, engaging in conversations with personas like friends, librarians, movie rental store attendants, or fellow cinephiles is an enjoyable experience that brings new perspectives to people's movie choices. Our work not only addresses fundamental conversational recommendation tasks but also incorporates variations in the roles of recommenders. This allows us to offer advice to users from different angles, enhancing the element of surprise. For example, engaging in discussions with a Marvel fan is more likely to result in recommendations for Marvel movies, whereas interactions with a librarian could lead to suggestions for films adapted from books. These distinct roles bring diverse recommendation influences, thereby augmenting diversity and contributing to heightened user satisfaction. That’s the reason why we integrate the recommenders’ historical experiences.\n\nFurthermore, it's important to acknowledge that the effectiveness of existing methods can be constrained by the limited context available in ongoing conversations. Indeed, selecting the most suitable movie for a user from hundreds or thousands of options based solely on the limited conversation is a challenging task. Our approach takes a crucial step toward addressing this limitation. By incorporating role-specific recommender experiences and enabling the retrieval of pertinent information, our method enhances contextual awareness even within the constraints of a brief dialogue. This enhancement, in turn, results in a notable improvement in the performance of Recall@1.\n\nIn conclusion, the practical significance of employing such methods in real-world applications lies in their ability to infuse engagement, novelty, and personalization into recommendation services while simultaneously addressing the challenges of limited context. Through the integration of diverse recommenders' roles and experiences, we create a more dynamic and effective recommendation experience for users.", "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": "New methods having better performance.", "reasons_to_reject": "The evaluation of the generation performance is questionable. The authors primarily present Dist-2, 3, and 4 scores in Table 1 as indicators of generation quality. Nevertheless, based on my personal experience, the generated sentences may exhibit considerable diversity (reflected in high Dist-n scores), yet still exhibit subpar readability and overall quality. Consequently, relying solely on dist-n scores may prove insufficient. To ensure a comprehensive comparison, it would be neccessary to incorporate additional generation metrics like BLEU alongside dist-n scores.\n\nBeyond automated metrics, it is imperative to complement the evaluation of generation performance with human assessments to substantiate the efficacy of the proposed method.\n\nIt would be valuable for the authors to provide supplementary experiments that further underscore the effectiveness of their proposed methods. While the ablation study and case study are informative, there exists a curiosity regarding the nuanced performance variations resulting from the application of the proposed mechanisms. It is important to recognize that enhancing performance in isolation may lack significance, particularly if the resultant recommendations are not practically applicable or realistic.", "questions_to_authors": "Can the authors explain the concept of an \"open-book prompt\" and the reason for using the term \"open-book\" to describe it?\n\nCould the authors explain why they chose RoBERTa in section 3.2.1 and selected DialoGPT as the base model for generation? Why were other pretrained language models not considered? Have the authors assessed the performance of other PLMs?\n\nThe recall@1 score for recommendation currently stands at approximately 5.7%, indicating a rather modest level of effectiveness. In light of this, is there a practical significance to employing such methods in real-world applications?", "paper_topic_and_main_contributions": "This paper introduces a novel method for conversational recommendation through the utilization of prompts. Experimental results conducted on the ReDial datasets demonstrate the notable advantages of the proposed approach.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "4xp7JKT23f", "reviewer": "Reviewer_1wDy", "summary": "", "questions": "", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1.\tThe paper is positioned in a confusing way. The authors sell this paper as a prompt-based approach to primarily leverage pre-trained language models, but the bulk of the method aims to learn a recommender system that is then used to train the LM prefix module/weights. This amounts to a multi-part recommender system like traditional approaches to conversational recommendation.\n\n**Response:** Thank you for bringing this concern to our attention. We apologize for any confusion caused by our explanation of the architecture, but we respectfully disagree with this comment. Our method indeed follows a prompt-based approach for conversational recommendation task. The rationale behind this approach lies in the ability of prompt to unify the formats of both response generation subtask and item recommendation subtask.\n\nOur primary innovation is centered around the construction of the prompt. We utilize the representation of the recommender as the base prompt, allowing us to maximize the utilization of information from the recommendation scenario. Given the limited context within the current utterance, we propose leveraging the recommender's past successful recommendations as an open-book knowledge store, referencing it during inference. Subsequently, we employ three strategies to retrieve historical item recommendation experiences relevant to the ongoing dialogue, thereby enhancing targeting. This enhancement strengthens the representation of the base prompt, infusing it with information about the labels of potentially recommended items. As a result, the language model's capability for the recommendation task is enhanced. Our approach distinctively aims to integrate the recommendation scenario knowledge into the prompt, thereby enabling effective conversational recommendation.\n\n\n2.\tThis paper would have benefited from a comparison against natural language prompting (or in-context examples drawn from the same training example [open book] selection methodology to derive the prefix) of a pre-trained language model. This would have better justified the decision to use prefix-tuning, etc. Additionally, the assertion that \"discrete prompts' impact on model performance is not very significant\" (section 2) needs support, especially in light of the past few years' high success rates in adapting PLMs for various NLP/NLU tasks with discrete prompts.\n\n**Response:** We thank the reviewer for pointing out this issue and we deeply apologize for the absoluteness of the statement in Section 2, which might have led to misunderstandings. Firstly, UniCRS has integrated natural language prompting for the recommendation task, and we use it as our baseline for comparison. Secondly, we intended to convey that the impact of manually designed discrete prompts can vary significantly. Even substituting a single word within a manually designed prompt can result in substantial variations in performance. It's important to note that without meticulously crafting manual prompts and carefully evaluating the impact of each word on performance, using a simplistic manual prompt might not yield optimal results. We deeply value the guidance provided by the reviewer, and we are committed to revising the statement within our paper to better reflect these nuances.\n\n3. The choice of metric (solely distinct-n) for evaluating response generation is a bit confusing. There is no measure of fidelity, fluency, or faithfulness - only diversity? The metrics section is the first part in which engagingness is mentioned in earnest so it seems strange that no other metrics are compared. As such, the assertion that \"Your DOP model consistently outperforms other models across all metrics, demonstrating its superiority\" reads as disingenuous.\n\n**Response:** Thanks for your comments! We have taken your suggestions into serious consideration. Conversational recommender systems (CRS) aim to recommend high-quality items through natural language conversations. It consists of two subtasks: item recommendation and response generation. In the item recommendation subtask, our goal is to offer accurate recommendations to users. In the response generation subtask, our aim is to assist in the recommendation task with diverse responses. Therefore, the task emphasizes accuracy in item recommendation and seeks maximum diversity in response generation. Following previous CRS works such as UniCRS and KGSF, we adopt Distinct-𝑛 (𝑛=2,3,4) at the word level to evaluate the diversity of the generated responses.\n\nTo further verify the effectiveness of our model in terms of the generation quality, we have extended our evaluation metrics by incorporating the BLEU metric alongside the Distinct-𝑛 scores. \nWe present these additional metrics in Table I, comparing the performance of our proposed DOP model with the best-performing baseline, UniCRS. The experimental results clearly demonstrate our method maintains a strong performance in response generation accuracy, which proves that our DOP successfully captures the personal language habits of the recommender and integrates contextual information through the open-book prompt and is more effective for response generation.\n\n**Table I. Supplement results on the conversation task.**\n|           | Dist-2 | Dist-3 | Dist-4 | BLEU-2 | BLEU-3 | BLEU-4 |\n|:----------|-------:|-------:|-------:|-------:|-------:|-------:|\n| UniCRS    |  0.437 |  0.689 |  0.880 |  0.055 |  0.029 |  0.017 |\n| DOP       |  0.876 |  1.428 |  1.827 |  0.057 |  0.034 |  0.021 |\n\n\nMoreover, according to your suggestion, we have gone a step further by conducting human evaluations to complement the automated metrics. Following UniCRS, we invite three annotators to score the generated responses of our model and the best-performing baseline for 200 utterances from two aspects, namely Fluency and Informativeness. The range of scores is 0 to 2. Fluency measures how likely the generated text is produced by human. Informativeness measures whether the response provides new information and knowledge in addition to the post. The results are shown in the Table II below. As can be seen, the responses we generated are both fluent and rich in information. These results prove that the recommender’s experiences as an open-book store help the model effectively understand the dialogue history, and generate fluent and informative responses.\n\n**Table II. Supplement results of human evaluations.**\n|         | Fluency | Informativeness |\n|---------|-------:|---------------:|\n| UniCRS  |   1.39  |           0.97 |\n| DOP     |   1.41  |           1.04 |\n\n\n4.\tThe emphasis on recommendation (e.g. ablation was only conducted for recommendation subtask, the text generation metrics seeming like an afterthought) implies that EMNLP may not be the right venue for this paper; instead it may be better positioned for CIKM or RecSys?\n\n**Response:** Thank you for your suggestion. Conversational recommender systems aim to provide recommendations to users through natural language interactions. The foundation lies in the idea that engaging in discussions with personas like friends, librarians, movie rental store attendants, or fellow cinephiles can enrich people's movie choices. This task inherently embraces both recommendation and dialogue elements, with a focus on enhancing the precision of recommended items and diversifying the response generation process. It presents a comprehensive challenge that encompasses both recommendation and conversation domains.\n\nMoreover, our adoption of a prompt-based architecture seeks to leverage the knowledge of pre-trained language models as much as possible, which aligns with natural language processing tasks. While our paper indeed accentuates the recommendation aspect, we also utilize extracted information as prompt to effectively integrate the item recommendation and response generation subtasks within the context of natural language interactions. This approach significantly contributes to its relevance within the scope of EMNLP. We genuinely appreciate your insights concerning potential publication venues, and we firmly believe that our work aligns with the themes and goals of this conference.\n\n5. Leading diagram (Figure 1) is noisy, with small font. Could clean this up and improve readability.\n\n**Response:** Thank you for your suggestion. We will continue to refine the figure to enhance its readability.", "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": "- The DOP/dynamic open book approach makes sense intuitively re: adding additional knowledge outside of the conversation context making use of some form of relevance ranking.\n- The authors report statistically significant gains in recommendation tasks for DOP over multiple classes of baselines.\n- The authors conduct an ablation study to justify their architecture and modeling choices.", "reasons_to_reject": "- The paper is positioned in a confusing way. The authors sell this paper as a prompt-based approach to primarily leverage pre-trained language models, but the bulk of the method aims to learn a recommender system that is then used to train the LM prefix module/weights. This amounts to a multi-part recommender system like traditional approaches to conversational recommendation.\n- This paper would have benefited from a comparison against natural language prompting (or in-context examples drawn from the same training example [open book] selection methodology to derive the prefix) of a pre-trained language model. This would have better justified the decision to use prefix-tuning, etc. Additionally, the assertion that \"discrete prompts' impact on model performance is not very significant\" (section 2) needs support, especially in light of the past few years' high success rates in adapting PLMs for various NLP/NLU tasks with discrete prompts.\n- The choice of metric (solely distinct-n) for evaluating response generation is a bit confusing. There is no measure of fidelity, fluency, or faithfulness - only diversity? The metrics section is the first part in which engagingness is mentioned in earnest so it seems strange that no other metrics are compared. As such, the assertion that \"Your DOP model consistently outperforms other models across all metrics, demonstrating its superiority\" reads as disingenuous.\n- The emphasis on recommendation (e.g. ablation was only conducted for recommendation subtask, the text generation metrics seeming like an afterthought) implies that EMNLP may not be the right venue for this paper; instead it may be better positioned for CIKM or RecSys?", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper is focused on conversational recommender systems and specifically aims to improve prompt-based approaches to conversational recommender systems (prompting pre-trained language models to elicit recommendations and dialog utterances). The authors propose to use prefix-tuning guided by a combination of training corpus utterance retrieval and ranking.", "excitement": 2, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "Leading diagram (Figure 1) is noisy, with small font. Could clean this up and improve readability."}, "extra_text": {}}, {"review_id": "KQQ8TEwaFR", "reviewer": "Reviewer_MJfa", "summary": "", "questions": "In terms of the architecture, is there any use for the dialogue history in addition to the current user utterance?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1. The paper lacks clarity in multiple parts. The architecture is presented in a very fragmented way (see Presentation Improvements for more details), and concepts are ill-defined. Importantly, it is never perfectly clear what exactly the \"recommender nodes\" are --- illustrating a single node and the actual graph would be helpful, even as an Appendix. Consequently, authors use expressions like \"this enables direct access to historical information\" (lines 539-540), and readers can only assume that \"historical information\" is defined as past recommender runs --- even though there could be multiple interpretations to \"historical information.\"\n\n**Response:** Thank you for the detailed comments. We apologize for not adequately explaining the relevant concepts, which has constrained the readability of our paper. The recommender-item graph we constructed is a heterogeneous bipartite graph, comprising nodes representing recommenders and items. The edges signify whether the recommender successfully recommended the item in the training set dialogues. If a recommender successfully recommended an item in the training set, an edge connects the recommender node to the item node with an edge weight of 1. The term \"historical information\" refers to the historical recommender-item recommendation results in the training set. During inference, the prompt can retrieve relevant recommender-item interactions from the graph to provide more effective guidance. We hope this provides a clearer understanding of the graph construction.\n\n2. The second part of the results, measured in terms of Dist-2, Dist-3, and Dist-4, is not particularly convincing. Given the more dynamic nature of prompt learning, there will be higher prompt variation; therefore, it is highly expected that there will be greater natural language variation at generation. For this reason, Dist-n should not be the measure to differentiate between methods. Instead, the evaluation question should be: How helpful is this increased natural language variation? Authors hint on this question with two qualitative examples on Table 2, but aspects like helpfulness/informativeness should be more quantitatively measured --- for example, by using human judges on a certain sample of responses.\n\n**Response:** Many thanks for your suggestion! We have taken your suggestions into serious consideration. Conversational recommender systems (CRS) aim to recommend high-quality items through natural language conversations. It consists of two subtasks: item recommendation and response generation. In the item recommendation subtask, our goal is to offer accurate recommendations to users. In the response generation subtask, our aim is to assist in the recommendation task with diverse responses. Therefore, the task emphasizes accuracy in item recommendation and seeks maximum diversity in response generation. Following previous CRS works such as UniCRS and KGSF, we adopt Distinct-𝑛 (𝑛=2,3,4) at the word level to evaluate the diversity of the generated responses.\n\nTo further verify the effectiveness of our model in terms of the generation quality, we have extended our evaluation metrics by incorporating the BLEU metric alongside the Distinct-𝑛 scores.  We present these additional metrics in Table I, comparing the performance of our proposed DOP model with the best-performing baseline, UniCRS. The experimental results clearly demonstrate our method maintains a strong performance in response generation accuracy, which proves that our DOP successfully captures the personal language habits of the recommender and integrates contextual information through the open-book prompt and is more effective for response generation.\n\n**Table I. Supplement results on the conversation task.**\n|           | Dist-2 | Dist-3 | Dist-4 | BLEU-2 | BLEU-3 | BLEU-4 |\n|:----------|-------:|-------:|-------:|-------:|-------:|-------:|\n| UniCRS    |  0.437 |  0.689 |  0.880 |  0.055 |  0.029 |  0.017 |\n| DOP       |  0.876 |  1.428 |  1.827 |  0.057 |  0.034 |  0.021 |\n\n\nMoreover, according to your suggestion, we have gone a step further by conducting human evaluations to complement the automated metrics. Following UniCRS, we invite three annotators to score the generated responses of our model and the best-performing baseline for 200 utterances from two aspects, namely Fluency and Informativeness. The range of scores is 0 to 2. Fluency measures how likely the generated text is produced by human. Informativeness measures whether the response provides new information and knowledge in addition to the post. The results are shown in the Table II below. As can be seen, the responses we generated are both fluent and rich in information. These results prove that the recommender’s experiences as an open-book store help the model effectively understand the dialogue history, and generate fluent and informative responses.\n\n**Table II. Supplement results of human evaluations.**\n|         | Fluency | Informativeness |\n|---------|-------:|---------------:|\n| UniCRS  |   1.39  |           0.97 |\n| DOP     |   1.41  |           1.04 |\n\n\n3. In terms of the architecture, is there any use for the dialogue history in addition to the current user utterance?\n\n**Response:** In addition to the current user utterance, the dialogue history is integrated in the form of a graph during the prompt construction process. Initially, we establish a recommender-item historical experience bipartite graph based on whether recommenders successfully recommended specific items in the dialogue history. This graph includes nodes representing recommenders and items. It's worth noting that this graph construction process does not consider the explicit user demands expressed within the dialogue. Then, we employ three strategies to retrieve the historical items associated with the ongoing dialogue from the graph to enhance the contextual understanding.\n\n4. Unlike Wang et. al (2022), it is unclear in the authors' paper whether and how the dialogue history is utilized. If we compare authors' Fig. 2 with Wang et. al's Fig. 1, it becomes clear that authors' framework description is excessively fragmented. Instead of being a framework description focused on isolated fragments of their architecture, an architecture description would be much more helpful. Considering how UniCRS is central to their architectural choices, authors could borrow Fig. 2 from Wang et al. and describe their contributions in the context of it.\n\n**Response:** Many thanks for your suggestion! We use the dialogue history to construct the recommender-item graph which serves as an open-book knowledge store. During inference, we incorporate the current user utterance to retrieve relevant items from the graph, thereby enriching the present context. We recognize the need for a more cohesive framework description and will take your suggestion to heart. To provide a clearer and more comprehensive understanding of our architecture, we plan to enhance our presentation. We will consider your advice to incorporate Fig. 2 from Wang et al.'s work and illustrate our contributions within that context. Once again, we thank you for your constructive feedback, which will undoubtedly contribute to the clarity and cohesiveness of our paper.\n\n5.\tWas the parameter study (subsection 4.5) on the validation set? Please specify.\n\n**Response:** Yes, the parameter study in subsection 4.5 was conducted on the validation set. Specifically, we trained the parameters on the training set, refined the performance by selecting the optimal parameters on the validation set and saved the best-performing model, and ultimately used this model to evaluate the results on the test data.", "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": "1. Conversational recommendation is an important task which has attracted increasing attention in the NLP community, particularly for the use of LLMs towards it.\n\n2. Authors test their methods on ReDial, a well-known dataset in this space with multiple baselines available and included in the paper.\n\n3. The first part of the results, measured in terms of Recall@k, is promising. While BOP approaches UniCRS, DOP is 11% better on Recall@1 and 8% better on Recall@50.", "reasons_to_reject": "1. The paper lacks clarity in multiple parts. The architecture is presented in a very fragmented way (see Presentation Improvements for more details), and concepts are ill-defined. Importantly, it is never perfectly clear what exactly the \"recommender nodes\" are --- illustrating a single node and the actual graph would be helpful, even as an Appendix. Consequently, authors use expressions like \"this enables direct access to historical information\" (lines 539-540), and readers can only assume that \"historical information\" is defined as past recommender runs --- even though there could be multiple interpretations to \"historical information.\"\n\n2. The second part of the results, measured in terms of Dist-2, Dist-3, and Dist-4, is not particularly convincing. Given the more dynamic nature of prompt learning, there will be higher prompt variation; therefore, it is highly expected that there will be greater natural language variation at generation. For this reason, Dist-n should not be the measure to differentiate between methods. Instead, the evaluation question should be: How helpful is this increased natural language variation? Authors hint on this question with two qualitative examples on Table 2, but aspects like helpfulness/informativeness should be more quantitatively measured --- for example, by using human judges on a certain sample of responses. ", "questions_to_authors": "In terms of the architecture, is there any use for the dialogue history in addition to the current user utterance?", "paper_topic_and_main_contributions": "The paper focuses on the conversational recommendation task, proposing methods to retrieve and leverage at inference time past recommender runs that are relevant to an incoming user utterance. Authors are strongly inspired by UniCRS (Wang et al., 2022), with the key differentiator being the use of past recommender runs (or \"past experiences\") as a \"dynamic open-book.\" To this end, authors propose three methods for fusing the retrieved past experience into the prompt representation learning process: enhancing the node embeddings from the recommender-item graph by fusing it with the current user utterance; enhancing the recommender-item graph structure by also fusing them with the current user utterance; enhancing the message-passing step by, again, fusing it with the current user utterance. A system based on DialoGPT (similar to Wang et al., 2022) and with the three methods is tested on the ReDial dataset, as well as ablations of this system with and without each method. Results generally favor the proposed system over UniCRS, with ~10% performance gains on topmost Recall@k.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "No particular reference missing."}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "Unlike Wang et. al (2022), it is unclear in the authors' paper whether and how the dialogue history is utilized. If we compare authors' Fig. 2 with Wang et. al's Fig. 1, it becomes clear that authors' framework description is excessively fragmented. Instead of being a *framework* description focused on isolated fragments of their architecture, an *architecture* description would be much more helpful. Considering how UniCRS is central to their architectural choices, authors could borrow Fig. 2 from Wang et al. and describe their contributions in the context of it.\n\nBetter definitions and using the Appendices would greatly benefit this paper (see Reasons to Reject, #1).\n\nWas the parameter study (subsection 4.5) on the validation set? Please specify."}}]

Author rebuttal:

Meta-review: This paper proposes a novel Dynamic Open-book Prompt approach for conversational recommendation, where the open book stores users' experiences in historical data, and the prompt is dynamically constructed to memorize the user's current utterance and selectively retrieve relevant contexts from the open book. Extensive experimental results on the ReDial dataset demonstrate the significant improvements achieved by the proposed model over the state-of-the-art methods.

All reviewers have pointed out that the evaluation of the generation performance is questionable by using only metrics Dist-n. Although the results of BLEU are added in the rebuttal, its validity should be justified given the multiple acceptable responses that can vary in syntax/lexicon, and some contributions also seem to be overclaimed. Reviewers further suggest using more recent and more powerful PLMs rather than DialoGPT in the experiments. Besides, reviewer MJfa mentioned that the paper lacks clarity in many parts, which need to be fixed.|meta review
Decision: Accept-Findings