Empowering Retrieval-based Conversational Recommendation with Contrasting User Preferences (2025)

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 pref- erences 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.

## introduction conversational recommender systems (crss) @xcite @xcite 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 @xcite , the recommendation task is yet challenging to address. in this paper, we thus focus on improving the recommendation task. existing crs methods @xcite @xcite 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 decisionmaking process @xcite . to address this issue, recent studies @xcite @xcite @xcite introduce preferenceaware crs models that extract user preferences from conversations across multiple sentiments or emotions, leveraging them for more precise item recommendations. for example, @xcite 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 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 al-lows us to directly associate both preference types to calculate the recommendation scores of items. the main contributions of our work are summarized as follows: 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. ## 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. ## related work traditional conversational recommender systems (crss) @xcite increasingly harness external information, such as knowledge graphs @xcite @xcite @xcite and metadata @xcite , to enhance domain knowledge. with the substantial impact of large language models (llms) demonstrating exceptional world knowledge, recent studies @xcite @xcite have focused on utilizing llms as standalone recommenders. in particular, several approaches @xcite 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 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 @xcite . revcore @xcite classifies entities from conversations as positive or negative and retrieves emotion-related reviews to enhance user expressiveness. ecr @xcite leverages llms to categorize entities into nine specific emotional categories, segmenting the user preferences. memo-crs @xcite 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 @xcite @xcite 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 @xcite 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 @xcite , offering high generalizability and scalability. ## 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 (