Conversational Recommender Systems (CRSs) aim to extract user preferences from natural language interactions and deliver personalized recommendations through conversations. In multi-turn conversations, these systems often generate low-confidence responses when user intent is unclear, leading to inappropriate replies and a degraded user experience. However, existing CRSs rarely assess the confidence of generated responses or detect their semantic uncertainty. Additionally, limited modeling of item-based semantic relevance hinders the system’s ability to produce contextually coherent and preference-aligned recommendations. To this end, we propose a Semantic enhanced Conversational Recommender System (SCRS) that explicitly models response-based semantic uncertainty and item-based semantic relevance to enhance both response and recommendation quality. Specifically, in conversation tasks, SCRS samples multiple candidate responses and estimates their semantic uncertainty by combining generation probabilities with semantic analysis, serving as a constraint to guide fine-tuning and improve response quality. In recommendation tasks, SCRS enriches item representations by integrating textual knowledge and calculates semantic relevance between items, then applies a dynamic optimization strategy to refine the recommendation process, generating items better aligned with user preferences. Extensive experiments on two public datasets demonstrate that SCRS achieves significant improvements in both recommendation accuracy and response quality over state-of-the-art baselines, validating the effectiveness of our proposed method.
Chang-Bin Zhong, Jie Zou, Aixin Sun et al.· ACM Transactions on Informat...· 0 citations
Existing conversational retrievers commonly treat topical relevance as a proxy for answerability. However, a passage that closely matches the dialogue context is not necessarily the one that supports the correct answer. We identify this mismatch as a systematic answerability gap. To address this issue, we propose CLEAR, a framework that shifts conversational retrieval from topical relevance to answerability. The core of CLEAR is entailment distillation, which transfers answer-passage entailment supervision into a cross-encoder reranker so that the reranker discriminates answer-supporting passages from topical distractors at inference time, without requiring answers. CLEAR is complemented by a passage-centric abductive recall module that brings low-similarity yet answerable passages into the candidate pool by inferring answerable queries from passages with an LLM. Across TopiOCQA, QReCC, and out-of-domain TREC CAsT datasets, CLEAR consistently improves top-ranked precision over strong query-rewriting and dense-retrieval baselines, with the largest gains observed in conversations involving heavier topical noise. Moreover, applying our reranker on top of an LLM-driven query rewriter yields further gains.
Shuai Qin, Guo-Jia An, Wei-Kang Guo et al.· 0 citations
Large Language Models (LLMs) have demonstrated strong potential in recommendations due to their powerful reasoning capabilities. However, existing methods typically rely on a single reasoning path to drive the entire Top-K recommendations. This paradigm is prone to reasoning path collapse, where limiting exploration of potentially superior and diverse reasoning paths within the LLMs space. As a result, both the accuracy and diversity of the recommendation outcomes are constrained. To address this issue, we propose a novel model, Divergent Reasoning for LLM-based Recommendation, named DivReason. Inspired by the structure of intellect theory, which emphasizes a two-stage cognitive process of divergent thinking followed by convergent thinking, DivReason is designed with two core components: the Divergent Reasoning Path Generation Module and the Reasoning Path Aggregation Module. In the first module, DivReason introduces a training-free form of controlled uncertainty to promote diverse reasoning, leveraging Monte Carlo Dropout and Directional Perturbation to expand exploration in the latent reasoning space. In the Reasoning Path Aggregation Module, we adaptively select a subset of high-quality reasoning paths from the entire path pool and aggregate them into a unified reasoning representation. Meanwhile, we further adopt an alternating reinforcement learning strategy to optimize the model, explicitly balancing accuracy and diversity during training. Extensive experimental results show that DivReason effectively mitigates the issue of reasoning path collapse, while improving both the accuracy and diversity of LLM-based recommendations.
Guojia An, Jie Zou, Yuhan Yang et al.· Annual International ACM SIG...· 1 citation
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