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Conversational Recommendation via Hierarchical Information Modeling

Published: 07 July 2022 Publication History

Abstract

Conversational recommendation system aims to recommend appropriate items to user by directly asking preference on attributes or recommending item list. However, most of existing methods only employ the flat item and attribute relationship, and ignore the hierarchical relationship connected by the similar user which can provide more comprehensive information. And these methods usually use the user accepted attributes to represent the conversational history and ignore the hierarchical information of sequential transition in the historical turns. In this paper, we propose Hierarchical Information-aware Conversational Recommender (HICR) to model the two types of hierarchical information to boost the performance of CRS. Experiments conducted on four benchmark datasets verify the effectiveness of our proposed model.

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    cover image ACM Conferences
    SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2022
    3569 pages
    ISBN:9781450387323
    DOI:10.1145/3477495
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    Published: 07 July 2022

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    Author Tags

    1. conversational recommendation
    2. graph representation learning
    3. reinforcement learning

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    View all
    • (2025)Augmentation with Neighboring Information for Conversational RecommendationACM Transactions on Information Systems10.1145/371258843:3(1-49)Online publication date: 17-Jan-2025
    • (2025)Efficient and Effective Role Player: A Compact Knowledge-grounded Persona-based Dialogue Model Enhanced by LLM DistillationACM Transactions on Information Systems10.1145/371185743:3(1-29)Online publication date: 10-Jan-2025
    • (2024)MealRec+: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and HealthinessProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657857(564-574)Online publication date: 10-Jul-2024
    • (2024)Conversational Recommendation With Online Learning and Clustering on Misspecified UsersIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342344236:12(7825-7838)Online publication date: Dec-2024
    • (2024)Improving conversational recommender systems via multi-preference modelling and knowledge-enhancedKnowledge-Based Systems10.1016/j.knosys.2023.111361286:COnline publication date: 17-Apr-2024
    • (2023)U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591878(2723-2732)Online publication date: 19-Jul-2023
    • (2023)Towards Building Voice-based Conversational Recommender Systems: Datasets, Potential Solutions and ProspectsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591876(2701-2711)Online publication date: 19-Jul-2023

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