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Towards Topic-Guided Conversational Recommender System
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Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. To develop an effective CRS, the support of high-quality datasets is essential. Existing CRS datasets mainly focus on immediate requests from users, while lack proactive guidance to the recommendation scenario. In this paper, we contribute a new CRS dataset named \textbf{TG-ReDial} (\textbf{Re}commendation through \textbf{T}opic-\textbf{G}uided \textbf{Dial}og). Our dataset has two major features. First, it incorporates topic threads to enforce natural semantic transitions towards the recommendation scenario. Second, it is created in a semi-automatic way, hence human annotation is more reasonable and controllable. Based on TG-ReDial, we present the task of topic-guided conversational recommendation, and propose an effective approach to this task. Extensive experiments have demonstrated the effectiveness of our approach on three sub-tasks, namely topic prediction, item recommendation and response generation. TG-ReDial is available at https://github.com/RUCAIBox/TG-ReDial.
Forward citations
Cited by 6 Pith papers
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The paper contributes alternative-item relevance judgments for fashion CRS targets and two meta-simulators that let users switch targets, reporting that alternative-aware evaluation raises measured CRS effectiveness.
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CESRec: Constructing Pseudo Interactions for Sequential Recommendation via Conversational Feedback
CESRec improves sequential recommenders by converting simulated user feedback into edited pseudo-interaction sequences and masking outlier items, with gains reported on three benchmarks.
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On Mitigating Data Sparsity in Conversational Recommender Systems
DACRS combines LLM-based dialogue augmentation, knowledge-graph entity substitution, and an entity similarity constraint to improve conversational recommendation accuracy on ReDial and Inspired.
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A Framework for Generating Conversational Recommendation Datasets from Behavioral Interactions
ConvRecStudio generates roughly 38K synthetic multi-turn recommendation dialogs across three domains from historical interactions, and a cross-attention transformer fusing history with dialog beats dialog-only and his...
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HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation
HyCoRec adds multi-hypergraph preference fusion to a conversational recommender and reports higher coverage/lower isolation on REDIAL and TG-REDIAL, but Matthew-effect alleviation is not dynamically evaluated.
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Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation
HiCore, a triple-channel multi-hypergraph model with self-supervised learning, reports new state-of-the-art results on four conversational recommendation datasets and lower popularity bias, though its own tables contr...
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