short-paper

Conversational Recommendation via Hierarchical Information Modeling

Authors:

Rui YanAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2201 - 2205

https://doi.org/10.1145/3477495.3531830

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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Cited By

Liu YPei JZhang WLi MChe Wde Rijke M(2025)Augmentation with Neighboring Information for Conversational RecommendationACM Transactions on Information Systems10.1145/371258843:3(1-49)Online publication date: 17-Jan-2025
https://dl.acm.org/doi/10.1145/3712588
Hu LZhang XSong DZhou CHe HNie L(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
https://dl.acm.org/doi/10.1145/3711857
Li MLi LTao XHuang JHui Yang GWang HHan SHauff CZuccon GZhang Y(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
https://dl.acm.org/doi/10.1145/3626772.3657857
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Index Terms

Conversational Recommendation via Hierarchical Information Modeling
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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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

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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Published: 07 July 2022

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Liu YPei JZhang WLi MChe Wde Rijke M(2025)Augmentation with Neighboring Information for Conversational RecommendationACM Transactions on Information Systems10.1145/371258843:3(1-49)Online publication date: 17-Jan-2025
https://dl.acm.org/doi/10.1145/3712588
Hu LZhang XSong DZhou CHe HNie L(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
https://dl.acm.org/doi/10.1145/3711857
Li MLi LTao XHuang JHui Yang GWang HHan SHauff CZuccon GZhang Y(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
https://dl.acm.org/doi/10.1145/3626772.3657857
Dai XWang ZXie JLiu XLui J(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
https://doi.org/10.1109/TKDE.2024.3423442
Zhang CHuang XAn JZou S(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
https://dl.acm.org/doi/10.1016/j.knosys.2023.111361
Liu YZhang WDong BFan YWang HFeng FChen YZhuang ZCui HLi YChe WChen HDuh WHuang HKato MMothe JPoblete B(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
https://dl.acm.org/doi/10.1145/3539618.3591878
Qu XLiu HSun ZYin XOng YLu LMa ZChen HDuh WHuang HKato MMothe JPoblete B(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
https://dl.acm.org/doi/10.1145/3539618.3591876

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