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C²-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System

Published: 15 February 2022 Publication History

Abstract

Conversational recommender systems (CRS) aim to recommend suitable items to users through natural language conversations. For developing effective CRSs, a major technical issue is how to accurately infer user preference from very limited conversation context. To address issue, a promising solution is to incorporate external data for enriching the context information. However, prior studies mainly focus on designing fusion models tailored for some specific type of external data, which is not general to model and utilize multi-type external data. To effectively leverage multi-type external data, we propose a novel coarse-to-fine contrastive learning framework to improve data semantic fusion for CRS. In our approach, we first extract and represent multi-grained semantic units from different data signals, and then align the associated multi-type semantic units in a coarse-to-fine way. To implement this framework, we design both coarse-grained and fine-grained procedures for modeling user preference, where the former focuses on more general, coarse-grained semantic fusion and the latter focuses on more specific, fine-grained semantic fusion. Such an approach can be extended to incorporate more kinds of external data. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach in both recommendation and conversation tasks.

Supplementary Material

MP4 File (WSDM22-wsdmfp778.mp4)
This is a presentation video of our paper: C^2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System. In this paper, we proposed a novel contrastive learning based coarse-to-fine pre-training approach for conversational recommender system.

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  • (2025)The future of cognitive strategy-enhanced persuasive dialogue agents: new perspectives and trendsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-024-40057-x19:5Online publication date: 1-May-2025
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      cover image ACM Conferences
      WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
      February 2022
      1690 pages
      ISBN:9781450391320
      DOI:10.1145/3488560
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      Published: 15 February 2022

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      Author Tags

      1. contrastive learning
      2. conversational recommender system

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      • (2025)Conversational Recommendations With User Entity Focus and Multi-Granularity Latent Variable EnhancementIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352328337:3(1126-1139)Online publication date: Mar-2025
      • (2025)Sentimentally enhanced conversation recommender systemComplex & Intelligent Systems10.1007/s40747-024-01766-911:2Online publication date: 8-Jan-2025
      • (2025)The future of cognitive strategy-enhanced persuasive dialogue agents: new perspectives and trendsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-024-40057-x19:5Online publication date: 1-May-2025
      • (2024)Knowledge-Enhanced Conversational Recommendation via Transformer-Based Sequential ModelingACM Transactions on Information Systems10.1145/367737642:6(1-27)Online publication date: 18-Oct-2024
      • (2024)FairCRS: Towards User-oriented Fairness in Conversational Recommendation SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688150(126-136)Online publication date: 8-Oct-2024
      • (2024)Unleashing the Retrieval Potential of Large Language Models in Conversational Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688146(43-52)Online publication date: 8-Oct-2024
      • (2024)Triple Sequence Learning for Cross-domain RecommendationACM Transactions on Information Systems10.1145/363835142:4(1-29)Online publication date: 9-Feb-2024
      • (2024)Broadening the View: Demonstration-augmented Prompt Learning for Conversational RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657755(785-795)Online publication date: 10-Jul-2024
      • (2024)Improving Conversational Recommendation System Through Personalized Preference Modeling and Knowledge GraphIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342158036:12(8529-8540)Online publication date: Dec-2024
      • (2024)Improving Conversational Recommender System Via Contextual and Time-Aware Modeling With Less Domain-Specific KnowledgeIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339732136:11(6447-6461)Online publication date: Nov-2024
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