research-article

Multi-interest Diversification for End-to-end Sequential Recommendation

Authors:

Maarten De RijkeAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 1

Article No.: 20, Pages 1 - 30

https://doi.org/10.1145/3475768

Published: 08 September 2021 Publication History

Abstract

Sequential recommenders capture dynamic aspects of users’ interests by modeling sequential behavior. Previous studies on sequential recommendations mostly aim to identify users’ main recent interests to optimize the recommendation accuracy; they often neglect the fact that users display multiple interests over extended periods of time, which could be used to improve the diversity of lists of recommended items. Existing work related to diversified recommendation typically assumes that users’ preferences are static and depend on post-processing the candidate list of recommended items. However, those conditions are not suitable when applied to sequential recommendations. We tackle sequential recommendation as a list generation process and propose a unified approach to take accuracy as well as diversity into consideration, called multi-interest, diversified, sequential recommendation. Particularly, an implicit interest mining module is first used to mine users’ multiple interests, which are reflected in users’ sequential behavior. Then an interest-aware, diversity promoting decoder is designed to produce recommendations that cover those interests. For training, we introduce an interest-aware, diversity promoting loss function that can supervise the model to learn to recommend accurate as well as diversified items. We conduct comprehensive experiments on four public datasets and the results show that our proposal outperforms state-of-the-art methods regarding diversity while producing comparable or better accuracy for sequential recommendation.

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Index Terms

Multi-interest Diversification for End-to-end Sequential Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 1

January 2022

599 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3483337

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 08 September 2021

Accepted: 01 June 2021

Revised: 01 April 2021

Received: 01 December 2020

Published in TOIS Volume 40, Issue 1

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Dutch Ministry of Education Culture and Science through the Netherlands Organisation for Scientific Research

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Liu SYang Y(2024)Multi-interest Distribution Based Diversified RecommendationProceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning10.1145/3654823.3654856(176-183)Online publication date: 29-May-2024
https://doi.org/10.1145/3654823.3654856
Shi CRen PFu DXin XYang SCai FRen ZChen Z(2024)Diversifying Sequential Recommendation with Retrospective and Prospective TransformersACM Transactions on Information Systems10.1145/365301642:5(1-37)Online publication date: 29-Apr-2024
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Li MLiu YJullien SAriannezhad MYates AAliannejadi Mde Rijke MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657835(924-934)Online publication date: 11-Jul-2024
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