Venue: NAACL 2025

# Empowering Retrieval-based Conversational Recommendation with

Contrasting User Preferences

 Heejin Kook1, Junyoung Kim1, Seongmin Park, Jongwuk Lee2

Sungkyunkwan University, Republic of Korea

{hjkook, junyoung44, psm1206, jongwuklee}@skku.edu

Footnote 1: Equal contribution.

###### Abstract

Conversational recommender systems (CRSs) are designed to suggest the target item that the user is likely to prefer through multi-turn conversations. Recent studies stress that capturing sentiments in user conversations improves recommendation accuracy. However, they employ a single user representation, which may fail to distinguish between contrasting user intentions, such as _likes and dislikes_, potentially leading to suboptimal performance. To this end, we propose a novel conversational recommender model, called **COntrasting user pReference expAnsion and Learning (CORAL)**. Firstly, CORAL extracts the user's hidden preferences through _contrasting preference expansion_ using the reasoning capacity of the LLMs. Based on the potential preference, CORAL explicitly differentiates the contrasting preferences and leverages them into the recommendation process via _preference-aware learning_. Extensive experiments show that CORAL significantly outperforms existing methods in three benchmark datasets, improving up to 99.72% in Recall@10. The code and datasets are available at [https://github.com/kookeej/CORAL](https://github.com/kookeej/CORAL).

## 1 Introduction

Conversational recommender systems (CRSs) Lu et al. (2021); Wang et al. (2022); Zhang et al. (2024); Li et al. (2024) deliver personalized recommendations by deeply understanding users' evolving contexts through multi-turn interactions. Typically, CRSs consist of the following two tasks: _recommendation task_ to provide items by identifying the user's intent from the text, and _generation task_ to offer a human-friendly response to the user. While recent LLMs have shown impressive performance in natural language generation He et al. (2023); Huang et al. (2024), the recommendation task is yet challenging to address. In this paper, we thus focus on improving the recommendation task.

Existing CRS methods Chen et al. (2019); Zhou et al. (2020); Wang et al. (2022) leverage external information (_e.g._, knowledge graphs) or LLMs' reasoning capabilities to understand dialogue context and recommend user's preferable items. While user conversations often contain both positive and negative user preferences, they assume that the entities in the dialogue history are positive. Capturing various user sentiments or emotions is critical for understanding hidden user preferences in the decision-making process Lerner et al. (2015); Zhang et al. (2024).

To address this issue, recent studies Lu et al. (2021); Zhang et al. (2024); Xi et al. (2024); Xu et al. (2021); Zhao et al. (2023) introduce preference-aware CRS models that extract user preferences from conversations across multiple sentiments or emotions, leveraging them for more precise item recommendations. For example, Zhang et al. (2024) estimated probabilities for nine preference labels for each user utterance and integrated these probabilities into a unified user representation. However, they failed to model the complicated relationship among the user, items, and individual preferences

Figure 1: Comparison between traditional preference modeling and contrasting preference-aware modeling.



by representing contrasting preferences with a single user representation.

In the conversation, users express various preferences with opposing intentions, such as like and dislike, which can be referred to as _contrasting preferences_. Figure 1 illustrates an example of the importance of differentiating contrasting preferences, where the number indicates the semantic distance between the user and the item. Although the user expresses a negative sentiment towards _"violent"_, existing preference-aware CRS models may still recommend _"Kill Bill (2003)"_, which contains a similar characteristic to the user utterance. In contrast, considering negative preferences directly enables us to recommend _"Spy (2015)"_, where the user may exhibit less negative sentiment toward _"violent"_.

Based on this observation, we ask the following key questions: _(i) How do we extract contrasting user preferences from the conversation? (ii) How do we learn the relationship between the contrasting preferences and the user/item?_

To this end, we propose a novel retrieval-based CRS framework, _COntrasting user pReference expAnsion and Learning (CORAL)_, which extracts and learns contrasting preferences. Specifically, it has two key components. Firstly, we utilize _contrasting preference expansion_ using the advanced reasoning power of LLMs to accurately distinguish contrasting preferences within user-system dialogues into positive (_i.e._, _like_) and negative (_i.e._, _dislike_) preferences. Then, the extracted preferences are augmented for the recommendation task to elicit the user's potential preferences. In this process, we utilize a dense retrieval model to extract users, items, and preferences, thus representing them within the same representation space.

Secondly, _preference-aware learning_ is used to capture two opposite user preferences to identify whether the user will like or dislike the given item, as depicted in Figure 1(b). Given the textual representations, which consist of dialogue and item descriptions, along with like/dislike preferences obtained from the contrasting preference expansion stage, we input them into an encoder and optimize item representations to be semantically closer to dialogue and like preferences while being pushed further apart from dislike preferences. We also use negative sampling to enable the model to distinguish between items that are difficult to classify based solely on conversation representation, enhancing preference representation. Therefore, it allows us to directly associate both preference types to calculate the recommendation scores of items.

The main contributions of our work are summarized as follows:

* **Recommendation-tailored Augmentation**: We extract contrasting user preferences to achieve effective user preference modeling. Leveraging the reasoning capabilities of LLMs, we identify complex preferences expressed in natural language within the dialogue and augment potential preferences using prompts tailored for recommendation tasks.
* **Preference-aware Recommender**: To directly involve contrasting user preferences, we explicitly model and learn the relationships among the users, preferences, and items. Explicitly separating both like and dislike preferences provides a rationale for the recommendations, enhancing the interpretability and transparency of our approach.
* **Comprehensive Validation**: Coral outperforms seven baselines across three datasets, improving up to 99.72% in Recall@10. Notably, the ablation study demonstrates the effectiveness of learning preference relationships by separating like/dislike from user preferences.

## 2 Preliminaries

### Problem Statement

Let \(u\) and \(i\) denote a user and an item of user set \({\cal U}\) and item set \({\cal I}\). Each item \(i\) contains a key-value list of metadata represented as \(\left\{(a_{m},v_{m})\right\}^{|i|}_{m=1}\), where \(a_{m}\) and \(v_{m}\) denote the textual attribute (_e.g._, Title) and the corresponding textual value (_e.g._, Frozen[2013]) of \(m\)-th metadata, respectively. Here, \(|i|\) represents the number of metadata entries associated with item \(i\). The dialogue history of \(u\) is denoted as \(c=\left\{(s_{t},u_{t})\right\}^{|ci|}_{t=1}\), where \(u_{t}\) is the utterance at \(t\)-th turn, \(|c|\) is the number of turns within \(c\) and \(s_{t}\) is the speaker at \(t\)-th turn, either the _user_, seeking an item or the _system_, providing personalized recommendations, respectively.

The goal of CRSs is to offer a set of candidate items to the user at the \(n\)-th turn, based on the dialogue history \(c=\left\{(s_{t},u_{t})\right\}^{n-1}_{t=1}\) and the available metadata.

### Retrieval-based CRSs

Retrieval-based CRS models Gupta et al. (2023) recommend items based on the similarity between 

the textual representations of the dialogue \(T_{c}\) and the item \(T_{m}\). Specifically, \(T_{c}\) is obtained by concatenating the speaker and utterance of every turn in the conversation (_e.g._, \(T_{c}\) = "[User] Hi there! ... [System] How about ..."). The similarity score \(\mathrm{sim}(c,i)\) between \(c\) and \(i\) is calculated as follows:

\[S(c,i)=\mathrm{sim}(\mathrm{Enc}(T_{c}),\mathrm{Enc}(T_{m})), \tag{1}\]

where \(\mathrm{sim}\) denotes the similarity function (_e.g._, dot product), and \(\mathrm{Enc}\) denotes the encoder-based language models such as BERT Devlin et al. (2019).

## 3 Proposed Model: Coral

This section presents a novel CRS framework Coral, as depicted in Figure 2, designed to differentiate between contrasting user preferences and effectively associate them with items.

### Contrasting Preference Expansion

Figure 2(a) illustrates the overall process of contrasting preference expansion. We perform the user-and the item-side preference expansion. The user-side expansion aims to distinguish and enhance contrasting preferences into like/dislike ones, and the item-side expansion addresses the discrepancy between dialogue and item metadata.

#### 3.1.1 User-side Expansion

User-side expansion distinguishes and infers user preferences embedded in the dialogue. We decompose the problem into two sub-tasks. First, we utilize an LLM to analyze the dialogue history and accurately extract the user's superficial preferences. These preferences serve as evidential input, enabling the LLM to infer additional implicit and potential preferences from the conversation Gao et al. (2024); Madaan et al. (2023).

Step 1: Superficial preference extractionFor a given user \(u\), we incorporate the dialogue into a prompt template \(P_{\mathrm{ext}}\) and input it into LLMs. We provide a detailed example in the Appendix B.

\[T^{\mathrm{ext}}_{l},T^{\mathrm{ext}}_{d}=f_{\mathrm{LLM}}(P_{\mathrm{ext}}, T_{c}), \tag{2}\]

where \(T^{\mathrm{ext}}_{l}\) and \(T^{\mathrm{ext}}_{d}\) represent lists of keyphrases that capture the like and dislike preferences of user \(u\), respectively. By leveraging the reasoning power of LLM, it is possible to analyze the context of dialogue history and accurately extract the user's meaningful surface preferences, allowing for precise distinction between likes and dislikes.

Step 2: Potential preference augmentationTo improve recommendation accuracy, we leverage the reasoning power of LLMs to augment the user's potential preferences. We use the extracted superficial preferences of user \(u\) as a rationale to infer

Figure 2: Overall architecture of CORAL. It comprises two components: (i) _Contrasting Preference Expansion_, which extracts superficial user preferences and augments potential preferences implicitly present in the conversation (Section 3.1); and (ii) _Preference-aware Learning_, which directly models the relationships among the user, contrasting preferences, and items (Section 3.2).

 

potential preferences that the user might like, following self-feedback approaches in previous studies (Madaan et al., 2023; Gao et al., 2024).

\[T_{l}^{\rm aug},T_{d}^{\rm aug}=f_{\mathrm{LLM}}(P_{\rm aug},T_{c},T_{l}^{\rm ext },T_{d}^{\rm ext}), \tag{3}\]

where \(T_{l}^{\rm aug}\) and \(T_{d}^{\rm aug}\) denote lists of keyphrases that infer the potential positive and negative preferences of user \(u\), respectively.

#### 3.1.2 Item-side Expansion

To enhance the representation of an item, we adopted review summaries. Item metadata often diverges from the nature of conversations due to the absence of preferences. The preferences in the review bridge the gap between user conversation and item metadata, making the item representations more expressive.

Specifically, we crawled reviews for item \(i\) from IMDb1 and selected the top-\(j\) reviews based on helpful votes. Using an LLM, we extracted and summarized common preferences from the reviews and identified keyphrases to optimize item expressiveness. This process condenses the representation, diminishes redundancy, and enhances contextual relevance.

Footnote 1: [https://www.imdb.com/](https://www.imdb.com/)

\[T_{r}^{\rm sum}=f_{\mathrm{LLM}}(P_{\rm summary},T_{\rm m},\{r\}_{i=1}^{j}), \tag{4}\]

where \(r\) denotes individual reviews that have been crawled, and the top-\(j\) reviews are inserted into the prompt \(P_{\rm summary}\), which is designed to summarize multiple reviews. The common preferences summary for all the generated reviews is \(T_{r}^{\rm sum}\), which is then processed through an LLM to extract keyphrases using prompt template \(P_{\rm keyphrase}\).

\[T_{r}^{\rm key}=f_{\mathrm{LLM}}(P_{\rm keyphrase},T_{r}^{\rm sum}). \tag{5}\]

Further explanation, along with concrete examples of this process, is provided in Appendix B.

### Preference-aware Learning

Existing studies (Lu et al., 2021; Zhang et al., 2024) utilize differentiated user preferences but a single representation to represent contrasting preferences. In contrast, we explicitly represent these preferences separately from the conversation and learn the relationship between them to engage the preferences in item scoring directly.

#### 3.2.1 Preference Modeling

**Representation.** The encoding function \(f_{\text{Enc}}\) takes the given dialogue history \(T_{c}\) as input and returns a vector representation of the dialogue history \(c\), denoted as \(\mathbf{h}_{c}=f_{\text{Enc}}(T_{c})\in\mathbb{R}^{d}\). Specifically, \(T_{c}\) is tokenized and passed through a text encoder(Enc). The last hidden states of all tokens are subsequently mean-pooled, followed by L2 normalization to derive \(\mathbf{h}_{c}\).

\[[t_{1},\ldots,t_{w}]=\text{Tokenize}(T_{c}) \tag{6}\] \[[\mathbf{o}_{1},\dots,\mathbf{o}_{w}]=\text{Enc}([t_{1},\ldots, t_{w}])\] (7) \[\mathbf{h}_{c}=\frac{\mathbf{o}_{c}}{\|\mathbf{o}_{c}\|_{2}}, \text{where $\mathbf{o}_{c}=\frac{1}{w}\sum_{i=1}^{w}\mathbf{o}_{i}$}, \tag{8}\]

where \(t\) is the token, \(w\) is the length of tokenized \(T_{c}\) and \(\mathbf{o}_{i}\in\mathbb{R}^{d}\) is the vector of last hidden state.

Similarly, for \(T_{l}^{\rm aug}\) and \(T_{d}^{\rm aug}\), we obtain the like/dislike preference representations, \(\mathbf{h}_{l}\) and \(\mathbf{h}_{d}\).

\[\mathbf{h}_{l}=f_{\text{Enc}}(T_{l}^{\rm aug})\text{ and }\mathbf{ h}_{d}=f_{\text{Enc}}(T_{d}^{\rm aug}). \tag{9}\]

Inspired by previous work (Hou et al., 2024; Lei et al., 2024), we concatenate item metadata and review information to obtain a review-enhanced item vector representation.

\[\mathbf{h}_{i}=f_{\text{Enc}}(T_{m}\oplus T_{r}^{\rm key}), \tag{10}\]

where the \(\oplus\) denotes concatenation.

Consequently, we obtain three user-side representations (_i.e._, \(\mathbf{h}_{c}\), \(\mathbf{h}_{l}\), and \(\mathbf{h}_{d}\)) and one item-side representation (_i.e._, \(\mathbf{h}_{i}\)).

**Similarity scoring.** To compute the final score, we linearly aggregate three scores--the similarity between the dialogue history and item, between like preference and item, and between dislike preference and item. The goal is to ensure that the desired item is close to the conversation context and the user's like preference while being far from the user's dislike preference. We extend Equation (1) as follows:

\[S(c,i)=\text{sim}(\mathbf{h}_{c},\mathbf{h}_{i})\\ +\alpha\cdot\text{ sim}(\mathbf{h}_{l},\mathbf{h}_{i})-\beta\cdot \text{ sim}(\mathbf{h}_{d},\mathbf{h}_{i}), \tag{11}\]

where \(\alpha,\beta\in(0,1]\) are hyperparameters representing the importance of the user's like and dislike preferences. Empirically, \(\alpha\) is set to 0.5, and \(\beta\) is adjusted in [0.1, 0.3] depending on the dataset.

 

This approach is a simple yet effective way of reflecting contrasting preferences, requiring no additional parameters. Furthermore, it enhances interpretability by intuitively revealing which preferences the recommended items are derived from, demonstrated in a case study (Section5.3).

#### 3.2.2 Training

**Hard negative sampling.** Hard negative sampling directly impacts the convergence and performance of dense retrieval models Xiong et al. (2021).

Our key contribution lies in utilizing hard negative sampling to enhance the representation of user preferences, especially for samples that are challenging to predict based on conversation alone. Specifically, we first compute the similarity between \({\bf h}_{c}\) and all item embeddings, then apply softmax to convert the similarity scores into a probability distribution. Using this distribution \(\left\{p_{c,i}\right\}_{i\in I}\), we sample a set of \(k\) negative items \({\mathcal{I}_{c}}^{-}\), resulting in a set of hard negative samples, further enriching the model's understanding of user preferences.

\[p_{c,i}=\frac{\exp(\mathrm{sim}({\mathbf{h}_{c}},{\mathbf{h}_{ i}}))}{\sum_{j\in\mathcal{I}}\exp((\mathrm{sim}({\mathbf{h}_{c}},{ \mathbf{h}_{j}}))}\] (12) \[{\mathcal{I}_{c}}^{-}=\left\{i_{1},i_{2},\ldots,i_{k}\right\}\] \[~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{ }~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{ }~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{ }~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} }~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} }

 



### Implementation Details

We utilize gpt-4o-mini for contrasting preference expansion. We used gpt-4o-mini-2024-07-18 in all of our experiments including baselines. We initialize the model parameters with NV-Embed (Lee et al., 2024) (NV-Emb.) where \(d=4096\), and we applied LoRA (Hu et al., 2022), a parameter-efficient fine-tuning technique for training. The parameter \(\alpha\) was set to 0.5, and \(\beta\) was set to 0.3, 0.1, and 0.2 for the Inspired, ReDIAL, and Pearl dataset, respectively. We set the batch size to 8 for Pearl, and 10 for Inspired and ReDial. For negative samples, we used 24 for Pearl and 16 for Inspired and ReDIAL. We use Adam optimizer (Kingma and Ba, 2015) with a learning rate of 5e-5 for Pearl and ReDIAL, and 1e-4 for Inspired. We adopt early stopping based on NDCG@10, with a patience of 3 for Pearl, and 5 for Inspired and ReDIAL. The temperature parameter \(\tau\) is set to 0.05. We set the maximum sequence length to 512 for items and conversations and 256 for likes and dislikes. The warm-up steps are set to 10% of one epoch. We set \(k=3\) for the review summarization, using 3 reviews per item. The prompts used in Contrasting Preference Expansion and examples are provided in Appendix B and detailed configurations for the baselines are provided in Appendix C.

## 5 Results and Analysis

### Overall Performance

Table 2 compares Coral against seven baselines across three datasets. Coral achieves state-of-the-art performance, improving Recall@50 and NDCG@50 over the best competitive baseline by an average of 43.87% and 48.59%, respectively. This demonstrates that it consistently captures and models user preferences across datasets with diverse characteristics. Notably, Coral outperforms ECR (Zhang et al., 2024), which enhances a single user representation with sentiment. This indicates that explicitly learning the relationships between preferences and users/items is more effective for user preference modeling.

RevCore (Lu et al., 2021) and ECR (Zhang et al., 2024), which utilize user sentiment, outperform UniCRS (Wang et al., 2022) in Inspired and ReDIAL but not in Pearl. One possible reason is relatively longer dialogues in Pearl, which demand a more contextual ability to capture conversational context. RevCore and ECR focus on sentiment extraction at the entity and utterance levels, respectively, making it challenging to capture sentiment considering the full context. In contrast, Coral identifies user sentiment at the conversation

Figure 3: Performance according to various model sizes in INSPIRED.



level, achieving a more comprehensive understanding of user preferences throughout the dialogue.

### In-depth Analysis

#### 5.2.1 Performance by Model Size

Figure 3 presents the performance of Coral with different backbone model sizes. We have two observations as follows. (i) The performance increases as the model size increases, _i.e._, BERT-base (110M) \(\rightarrow\) FlanT5-XL (1.5B) \(\rightarrow\) NV-Embed (7B). This is because larger models are better at capturing complex semantic relationships in text. Additionally, Coral leverages a dense retriever structure to fully exploit the capabilities of PLMs. (ii) Coral significantly improves performance even with a relatively small model. Both DPR and Coral (BERT-base) use the same backbone. Coral (BERT-base) shows a 39% performance gain in NDCG@50 and achieves comparable performance to similarly sized baselines, such as UniCRS and ECR. These reveal that Coral is a highly scalable, universal, and effective framework that can be applied to any model size.

#### 5.2.2 Zero-shot Performance

To investigate the effectiveness of user-side expansion, we compare the zero-shot performance of various models with and without leveraging the user's potential preferences. We evaluate zero-shot performance on a sparse retriever and two dense retrievers with varying sizes, as shown in Table 3. For '_w/o L_, \(D\)', we only utilize \(T_{c}\) for user-side textual representation without \(T^{\text{aug}}_{l}\), and \(T^{\text{aug}}_{d}\). '_w/ L_, \(D\)' utilizes \(T_{c},T^{\text{aug}}_{l},T^{\text{aug}}_{d}\) and the score is computed as Equation (11). For the item-side textual representation, we concatenated \(T_{m}\) with \(T_{r}\textsuperscript{key}\). Coral significantly improves performance across different backbones, demonstrating a 37% average gain in the zero-shot setting. These results confirm that contrasting preference expansion effectively improves recommendation performance by inferring potential user preferences. Appendix A.1 provides more detailed ablation for user-side expansion.

#### 5.2.3 Ablation Study

To understand the impact of each component of Coral on performance, we conducted an ablation study on Inspired, as illustrated in Table 4. First, we validate the effectiveness of contrasting preference expansion. '_w/o L_, \(D\)' means that \(T^{\text{aug}}_{l}\) and \(T^{\text{aug}}_{d}\) were not used in both train and inference. We can see that all three expansions (_i.e._, \(L\), \(D\), and \(R\)) contribute to performance, especially at a high ranking. It indicates that the expansions allow us to distinguish subtle preferences. We then investigate the effect of augmenting user's potential preferences. For '_w/o Aug_.', we use \(T^{\mathrm{ext}}_{l}\), \(T^{\mathrm{ext}}_{d}\) as the user's positive and negative preferences with only superficial preference extraction. We find that augmenting the potential preference significantly improves performance, yielding up to a 15.41% increase in Recall@10. It highlights the importance of underlying preference within the dialogue.

Lastly, we explore the effects of our proposed

\begin{table}
\begin{tabular}{c|c c|c c} \hline \hline Dataset & \multicolumn{4}{c}{Inspired} \\ \hline Variants & R@10 & R@50 & N@10 & N@50 \\ \hline Coral & **0.3481** & 0.5667 & **0.1827** & **0.2297** \\ \hline w/o \(L\), \(D\) & 0.3248 & **0.5767** & 0.1668 & 0.2226 \\ w/o \(R\) & 0.3167 & 0.5348 & 0.1710 & 0.2193 \\ w/o \(L\), \(D\), \(R\) & 0.2948 & 0.5633 & 0.1595 & 0.2196 \\ w/o \(Aug\). & 0.3016 & 0.5379 & 0.1663 & 0.2202 \\ \hline w/o \(Neg.\) & 0.2974 & 0.5692 & 0.1520 & 0.2115 \\ w/o \(PL\) & 0.1847 & 0.3616 & 0.1107 & 0.1491 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study of CORAL in INSPIRED. The best scores are in **bold**. \(L\), \(D\) and \(R\) denote \(T^{\text{aug}}_{l}\), \(T^{\text{aug}}_{d}\), and \(T^{\text{key}}_{r}\), respectively. Also, _Aug_., _Neg_., and \(PL\) mean potential preference augmentation, hard negative sampling, and preference-aware learning, respectively.

\begin{table}
\begin{tabular}{c|c|c c c c|c c c|c c c} \hline \hline Dataset & \multicolumn{4}{c|}{Pearl} & \multicolumn{4}{c|}{Inspred} & \multicolumn{4}{c}{RelHal} \\ \hline Retriever & \(L\), \(D\) & R@10 & R@50 & N@10 & N@50 & R@10 & R@50 & N@10 & N@50 & R@10 & N@50 \\ \hline BM25 & w/o & 0.0053 & 0.0269 & 0.0031 & 0.0076 & 0.0390 & 0.1486 & 0.0216 & 0.0444 & 0.0091 & 0.0361 & 0.0044 & 0.0104 \\  & w/ & **0.0079** & **0.0340** & **0.0045** & **0.0099** & **0.0448** & **0.1714** & **0.0297** & **0.0580** & **0.0235** & **0.0704** & **0.0113** & **0.0219** \\ \hline BERT & w/o & **0.0040** & 0.0203 & **0.0015** & 0.0049 & 0.0057 & 0.0410 & 0.0017 & 0.0100 & 0.0069 & 0.0270 & 0.0039 & 0.0084 \\  & w/ & 0.0031 & **0.0225** & **0.0015** & **0.0056** & **0.0086** & **0.0648** & **0.0032** & **0.0152** & **0.0087** & **0.0302** & **0.0048** & **0.0096** \\ \hline NV-Emb. & w/o & 0.0454 & 0.1323 & 0.0210 & 0.0397 & 0.1648 & 0.3943 & 0.0856 & 0.1374 & 0.0767 & 0.1885 & 0.0368 & 0.0613 \\  & w/ & **0.0569** & **0.1508** & **0.0286** & **0.0489** & **0.2276** & **0.4048** & **0.1140** & **0.1538** & **0.0872** & **0.2190** & **0.0408** & **0.0711** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The zero-shot performance of various language models depending on the presence or absence of the user’s potential preference. \(L\) and \(D\) mean \(T^{\text{aug}}_{l}\) and \(T^{\text{aug}}_{d}\), respectively.

 

preference-aware learning. '_w/o Neg._' is a variant that uses in-batch negative instead of hard negative, and '_w/o PL_' is a variant without preference modeling and hard negative, which utilizes single user representation by concatenating \(T_{c},T^{\text{aug}}_{l},T^{\text{aug}}_{d}\). Compared to Coral, removing negative sampling or preference modeling leads to a significant drop in performance. Hence, preference-aware learning effectively learns the relationship between conversation, preferences, and items. Refer to Appendix A.2 for the results of the ablation study on BERT, and Appendix A.3 for the results using different LLMs in Contrasting Preference Expansion.

### Case Study and Visualization

Figure 4(a) shows a case study of the user's dialogue, the augmented preferences, and the ground truth item from Pearl. The highlighted phrases in the same color represent phrases that belong to the same concept and are related to the ground-truth item. In particular, the blue, green, and orange preferences mean newly augmented through contrasting preference expansion.

Firstly, Coral effectively inferred the user's preference for _Thriller_ and _Drama_ genres based on the dialogue. This demonstrates that leveraging the augmentation effectively makes it possible to predict user preferences that are difficult to infer solely from the given dialogue history. Also, it can be observed that the user's preferences align with the item's review summary (_e.g._, _immersive sound design_ in user preference, _immersive atmosphere_ in item review summary). This indicates that the review summary successfully bridges the gap between the preferences expressed in the user conversation and the item. Additionally, Coral's contrasting preference expansion serves as a rationale for the recommendation results, thereby providing explainability.

We then visualized the corresponding example in the embedding space using t-SNE in Figure 4(b), to illustrate how Coral utilizes preference. Green, blue, and red dots are the embedding vectors of \(h_{c}\), \(h_{l}\), \(h_{d}\) for the dialogue shown in Figure 4(a). The X marks indicate the ground truth item and the orange dots represent negative items that are not the ground truth. The example shows incorrect items, such as those close to the dialogue but positioned toward the "dislike" items, that may be selected when only the conversation is used. This is because user conversations contain contrasting preferences. However, since Coral explicitly differentiates like/dislike preferences and models the relationship between the user and the item, it successfully recommended the correct item by distinguishing subtle differences.

## 6 Related Work

Traditional conversational recommender systems (CRSs) Jannach et al. (2021) increasingly harness external information, such as knowledge graphs Chen et al. (2019); Zhou et al. (2020); Petruzzelli et al. (2024); Wang et al. (2022); Zhou et al. (2023) and metadata Yang et al. (2022), to enhance domain knowledge. With the substantial impact of large language models (LLMs) demonstrating exceptional world knowledge, recent studies He et al. (2023); Liu et al. (2023); Li et al. (2024); Spurlock et al. (2024) have focused on utilizing LLMs as standalone recommenders. In particular, several approaches Zhao et al. (2021); Li et al. (2024) have been proposed to integrate the strong contextual understanding of LLMs with knowledge graphs to address gaps in domain-specific knowledge to improve the system's overall performance.

However, these studies commonly neglect the diversity of users' emotions and attitudes toward

Figure 4: Case study and visualization of CORAL in PEARL dataset.

 

entities in dialogues, which undermines the conversation's complexity and degrades user experience.

**Preference-aware CRSs.** Several studies have focused on improving user preferences at the entity level by considering user emotions using knowledge graphs Lu et al. (2021); Zhang et al. (2024). RevCore Lu et al. (2021) classifies entities from conversations as positive or negative and retrieves emotion-related reviews to enhance user expressiveness. ECR Zhang et al. (2024) leverages LLMs to categorize entities into nine specific emotional categories, segmenting the user preferences. MemoCRS Xi et al. (2024) employs a memory-enhanced approach to ensure preference continuity by tracking sequential user preferences. Although these studies consider user preferences at various levels of granularity and context, they still overlook the existence of contrasting preferences.

**Retrieval-based CRSs.** Recent work Hou et al. (2024); Lei et al. (2024); Gupta et al. (2023); Kemper et al. (2024) reformulated recommender systems as item retrieval tasks, fully utilizing the semantic understanding and matching capabilities of language models. In light of this, a few studies Gupta et al. (2023); Kemper et al. (2024) leveraging retrievers have been introduced to enhance CRS tasks. Specifically, they treat the conversation as a query and items as documents and utilize text-matching techniques such as BM25 Lin et al. (2021), offering high generalizability and scalability.

## 7 Conclusion

In this paper, we proposed a new CRS model Coral that distinguishes users' ambiguous preferences, implying conflicting intentions inherent in their likes and dislikes. To support the recommendation task, Coral expands users' preferences and leverages the reasoning capabilities of LLMs to learn the relationships between conversations, preferences, and items. Extensive experiments are conducted to evaluate the effectiveness of our preference expansion and learning strategy, confirming that our approach surpasses all baseline models in enhancing recommendation performance (Table 2). Coral can also be robustly and universally applied across various language models (Figure 3), and operates effectively in a zero-shot setting, demonstrating the reliability of our augmented user potential preferences (Tables 3 and 5).

### Limitation

The limitations of this study can be categorized into aspects: (i) training and inference inefficiency and (ii) reliance on large language models (LLMs).

Firstly, in the contrasting preference expansion, extracting user preferences from dialogue heavily depends on the reasoning power of LLMs, which presents a challenge. The accuracy of extracted preferences and the quality of potential preferences are significantly influenced by the performance, size, bias, and knowledge scope of the LLM. Consequently, it is essential to closely examine the impact of these LLM characteristics on the learning outcomes. Secondly, in this work, we trained NV-Embed (7B) with only 10M parameters using the parameter-efficient training technique LoRA Hu et al. (2022) and enhanced training efficiency through negative sampling. Despite these efforts, training time and computational costs remain high when using LLMs.

## Acknowledgments

This work was partly supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP)-ICT Creative Consilience Program grant funded by the Korea government (MSIT) (No. RS-2019-II190421, RS-2022-II220680, IITP-2025-RS-2020-II201821, IITP-2025-RS-2024-00360227).

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## Appendix A Further Study

### Effect of Preference Ablation on Zero-shot Performance

Table 5 shows the results of ablating the input information used as a query without training NV-Embed. _Avg. Gain_ refers to the average performance gain when additional preferences are added compared to using only \(T_{c}\). For the item-side textual representation, we concatenated \(T_{m}\) with \({T_{r}}^{\text{key}}\). Across all three benchmark datasets, using all of \(T_{c}\), \(T_{l}^{\mathrm{aug}}\), and \(T_{d}^{\mathrm{aug}}\) resulted in significant performance improvements. Additionally, consistently using either \(T_{c}\) and \(T_{l}^{\mathrm{aug}}\) or \(T_{c}\) and \(T_{d}^{\mathrm{aug}}\) also outperformed using only \(T_{c}\). (i) This demonstrates that the augmented data is of high quality and contributes positively to recommendation performance. (ii) Furthermore, it confirms that the proposed separate scoring method aligns well with the user's intent.

### Ablation Study on Small LM

Table 6 presents the results of an ablation study on BERT, a small language model with 110M parameters. Notably, the highest performance is achieved when all preference components are incorporated. The ablation study further confirms that the components of Coral contribute consistently to performance improvement, even when applied to small language models.

### Effect of LLM in Contrasting Preference Expansion

Table 7 shows the results of utilizing different LLMs in Contrasting Preference Expansion. Coral_Mistral and Coral_gtt-a-mini are variants that utilize Mistral-7B-Instruct-v0.2 and gpt-4o-mini as LLMs in the Contrasting Preference Expansion step, respectively, and Coral_w0__L,D,R_ is a variant that does not use an expanded preference (_i.e._, \(T_{l}^{\mathrm{aug}}\), \(T_{d}^{\mathrm{aug}}\), \(T_{r}^{\mathrm{key}}\)). Both variants utilizing different LLMs outperform UniCRS and ECR and generally achieve better performance than the variant without expanded preferences. These results highlight the effectiveness of Coral and further validate its scalability and generalizability.

### Zero-shot Performance of Different LLMs compared to Coral

Table 8 shows the performance comparison between Coral and large language models GPT-4o-mini and GPT-4o. We used





\begin{table}
\begin{tabular}{l|c c c c|c c c} \hline \hline Dataset & \multicolumn{4}{c|}{Inspired} & \multicolumn{3}{c}{ReDial} \\ \hline Variants & R@10 & R@50 & N@10 & N@50 & R@10 & R@50 & N@10 & N@50 \\ \hline Coral & **0.1219** & **0.3714** & 0.0625 & **0.1173** & **0.0856** & 0.2255 & **0.0446** & **0.0765** \\ \hline w/o _L, D_ & 0.0962 & 0.3429 & 0.0429 & 0.0968 & 0.0775 & **0.2287** & 0.0407 & 0.0754 \\ w/o \(R\) & 0.1133 & 0.2695 & **0.0752** & 0.1103 & 0.0749 & 0.2052 & 0.0405 & 0.0702 \\ w/o _L, D, R_ & 0.0876 & 0.3010 & 0.0450 & 0.0967 & 0.0764 & 0.2231 & 0.0403 & 0.0738 \\ \hline w/o _Neg._ & 0.0867 & 0.2629 & 0.0371 & 0.0757 & 0.0725 & 0.2151 & 0.0375 & 0.0697 \\ w/o _PL_ & 0.1076 & 0.2467 & 0.0516 & 0.0832 & 0.0774 & 0.2138 & 0.0403 & 0.0713 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study of CORAL in INSPIRED and REDIAL on BERT. The best scores are in **bold**. \(L\), \(D\) and \(R\) denote \(T^{\rm aug}_{l}\), \(T^{\rm aug}_{d}\), and \(T^{\rm key}_{r}\), respectively. Also, _Neg._ and _PL_ mean potential hard negative sampling and preference-aware learning, respectively.

\begin{table}
\begin{tabular}{l|c|c c c|c} \hline \hline Dataset & Input info. & R@10 & R@50 & N@10 & N@50 & Avg. Gain(\%) \\ \hline Pearl & \(C\) & 0.0476 & 0.1230 & 0.0229 & 0.0395 & - \\  & \(C\), \(L\) & 0.0560 & 0.1349 & 0.0303 & 0.0474 & **19.91\%** \\  & \(C\), \(D\) & 0.0481 & 0.1349 & 0.0224 & 0.0415 & **3.40\%** \\  & \(C\), \(L\), \(D\) & 0.0573 & 0.1481 & 0.0311 & 0.0504 & **26.05\%** \\ \hline Inspired & \(C\) & 0.1837 & 0.4133 & 0.1038 & 0.1545 & - \\  & \(C\), \(L\) & 0.2103 & 0.3949 & 0.1133 & 0.1534 & **4.62\%** \\  & \(C\), \(D\) & 0.2000 & 0.4154 & 0.1064 & 0.1527 & **2.68\%** \\  & \(C\), \(L\), \(D\) & 0.2205 & 0.4205 & 0.1166 & 0.1597 & **9.37\%** \\ \hline ReDIal & \(C\), & 0.0887 & 0.2067 & 0.0383 & 0.0640 & - \\  & \(C\), \(L\) & 0.0954 & 0.2325 & 0.0415 & 0.0710 & **9.83\%** \\  & \(C\), \(D\) & 0.0910 & 0.2129 & 0.0400 & 0.0665 & **3.48\%** \\  & \(C\), \(L\), \(D\) & 0.0986 & 0.2431 & 0.0427 & 0.0735 & **13.78\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Zero-shot performance for preference input variant. \(C\), \(L\), and \(D\) denote \(T_{c}\), \(T^{\rm aug}_{l}\), and \(T^{\rm aug}_{d}\), respectively.



gpt-4o-mini-2024-07-18 for GPT-4o-mini and gpt-4o-2024-08-06 for GPT-4o, with the latter being a larger model than GPT-4o-mini. The experimental results demonstrate that Coral outperforms existing large language models on both Pearl and Inspired, and in the case of ReDial, it significantly surpasses the LLMs in R@50 and N@50 metrics.

## Appendix B Contrasting Preference Expansion

### User-side Expansion

The prompt used for _user-side expansion_ is shown in Table 9. Figure 6 shows an example of the final results. Our augmentation method can enhance user preferences through reasoning power, even in cases where user preferences are scarcely revealed in the conversation. Experimentally, using the augmented potential preferences shows better performance than not using them, as demonstrated in Table 4. The datasets will be available upon acceptance.

### Item-side Expansion

For item-side expansion, as mentioned in Section 3.1, it is necessary to extract review summaries from crawled reviews and convert them into keyphrases. The prompt used in this process focuses on summarizing the common like/dislike preference information that users commonly associate with the item. The prompt used for _item-side expansion_ is shown in Table 10. These summaries are then transformed into keyphrases. Figure 5 provides several examples of the results.

## Appendix C Implementation Details

Traditional CRS modelsUnlike Inspired and ReDial, Pearl does not provide the knowledge graph traditional CRS requires. Therefore, we used DBpedia Spotlight Mendes et al. (2011) to extract entities from the dialogue, and movie entities were constructed using the movie entities from Inspired and ReDial. While RevCore Lu et al. (2021) utilizes review data crawled from IMDb, we used our own crawled review data to ensure a fair comparison in our experiments. ECR Zhang et al. (2024) requires emotion labels. For ReDIal, we used the provided labels, whereas for Pearl and Inspired, which lack emotion labels, we inferred them using an LLM. Table 11 details the prompt used for this inference. Additionally, since Pearl and Inspired do not incorporate user feedback, we applied uniform weights to all items for feedback-aware item reweighting in ECR.

g **LLM-based CRS models.** We utilize ChatGPT2 (gpt-4o-mini) as the backbone of LLVM CRS. It generates item titles based on user dialogue without any task-specific fine-tuning. Tables 12 and 13 present the prompts used for the LLM-based approach. We computed the average performance across two types of prompts for evaluation, as used in He et al. (2023); Li et al. (2024).

Footnote 2: [https://openai.com/](https://openai.com/)

Retrieval-based CRS modelsWe implemented BM25 Robertson and Walker (1994) using Pyserini Lin et al. (2021), and DPR Karpukhin et al. (2020) was implemented with the BERT-base model Devlin et al. (2019).

Figure 5: The examples of item-side expansion in Section 3.1





\begin{table}
\begin{tabular}{l|p{284.5pt}} \hline \hline
**Stage** & \\ \hline  & Given some popular reviews about Kids \{_title_\}, describe what people liked and disliked about the movie under [Like] and [Dislike], respectively. If there is nothing to mention about like/dislike, simply write “None.” under the corresponding tag. \\ _Review summarization_ & Here are some basic information about the movie and reviews about it: Title: \{_title_\} \\  & Genre: \{_genres_\} \\  & Cast: \{_cast_\} \\  & Director: \{_director_\} Reviews: \{_reviews_\} \\ \hline  & Below are the common [Like] and [Dislike] from users about the \{_title_\}. Based on this information, generate 5-8 keyphrases that represent user preferences and intentions for this movie, separated by commas. Do not include any other explanations or sentences. \\ _Keyphrases generation_ & Here is some basic information about the movie and users’ preferences information: \{_userInformation_\} The output format must strictly adhere to the following: [Like] keyphrases or descriptions separated by comma \\  & [Dislike] keyphrases or descriptions separated by comma \\ \hline \hline \end{tabular}
\end{table}
Table 10: Prompts for review summarization and keyphrases generation. Both _title_ and _userInformation_ are placeholders.

\begin{table}
\begin{tabular}{c|p{396.7pt}} \hline \hline
**Stage** & **Prompts** \\ \hline  & Given a dialogue history between the User and the System, find all aspects related to the movies the user currently seeks. Also, you must classify the preferences about each aspect, Like and Dislike. If there is nothing to mention about likes or dislikes, simply write ”None.” under the corresponding tag. \\ _Superficial preference extraction_ & Dialogue history: \{_dialogHistory_\} Response: \\  & Like] \{_keyphrases or descriptions separated by comma_\} \\  & [Dislike] \{_keyphrases or descriptions separated by comma_\} \\ \hline  & You are an advanced user’s profile generator. Based on the conversation and the user’s like/dislike preferences, use your reasoning to infer and expand upon the user’s potential preferences. Augment key phrases related to the user’s likes and dislikes, including preferences they may not have explicitly stated, to better guide personalized suggestions. If no explicit user preferences are provided, infer them from the conversation. Do not include any unrelated information; only state the user’s preferences. \\ _Potential preference augmentation_ & User’s preferences: \{_extractedPreferences_\} Conversation: \{_dialogHistory_\} \\  & Now, let’s get started! \{_Text_\} \\  & [Like] \{_Expanded keyphrases describing the user’s likes_\} \\  & [Dislike] \{_ Expanded keyphrases describing the user’s dislikes_\} \\ \hline \hline \end{tabular}
\end{table}
Table 9: Prompts for contrasting preference augmentation. Both _dialogHistory_ and _extractedPreferences_ are placeholders.



Figure 6: Step-by-step results applying the proposed contrasting preference expansion method to the PEARL, INSPIRED, and REDIAL datasets. “Extraction” corresponds to the superficial preference extraction phase, and ”Augmentation” refers to the potential preference augmentation phase.

 



\begin{table}
\begin{tabular}{c|l} \hline \hline
**Reference** & **Prompts** \\ \hline  & Pretend you are a movie recommender system. I will give you a conversation between a user and you (a recommender system), along with the related knowledge triplets. Based on this information, you reply to me with 50 recommendations without extra sentences. The format of the recommendation list is: no. title (year). \\  & Here is the conversation:\textbackslash{}:\left\{\textbf{_dialogHistory_}\right\}\] \\ Zero-shot (He et al., 2023) & You are an excellent conversational recommender that helps the user achieve recommendation-related goals through conversations. Given the dialogue history and a knowledge triplets, your task is to generate appropriate item recommendations for the dialogue. You reply me with 50 recommendations without extra sentences. The format of the recommendation list is: no. title (year). \\  & Dialogue history:\textbackslash{}:\left\{\textbf{_dialogHistory_}\right\}\] \\ ChatCRS (Li et al., 2023) & Knowledge triples:\textbackslash{}:\left\{\textbf{_knowledgeTriplets_}\right\}\] \\ \hline \hline \end{tabular}
\end{table}
Table 13: Prompts used for ChatCRS Li et al. (2024) recommendation. Adapted from the prompts used for movie recommendation in He et al. (2023); Li et al. (2024). We reported the average performance of these two prompts. Both _dialogHistory_ and _extractedPreferences_ are placeholders.