research-article

Sequential Recommendation with Decomposed Item Feature Routing

Authors:

Xu ChenAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2288 - 2297

https://doi.org/10.1145/3485447.3512101

Published: 25 April 2022 Publication History

Abstract

Sequential recommendation basically aims to capture user evolving preference. Intuitively, a user interacts with an item usually because of some specific feature, and user evolving preference is essentially determined by a series of important features along the time line. However, existing sequential models usually represent each item by a unified embedding, which fails to distinguish item features, let along modeling the feature sequences. To bridge this gap, in this paper, we propose a novel sequential recommender model by learning the key item feature sequences underlying user behaviors, which facilitates more focused model optimization and better recommendation performance. To achieve this goal, we firstly represent each item by explicit or latent features, and then build both soft and hard models to route optimal feature sequences. More specifically, in the soft model, we design a 2D attention mechanism, which simultaneously distinguishes the importances of the items in a sequence and the features for the same item. For the hard model, we regard the feature routing problem as a Markov decision process, and propose a reinforcement learning method to generate feature sequences, which can lead to the lowered negative log-likelihood. In the experiments, we compare our model with the state-of-the-art methods based on real-world datasets, where we can empirically demonstrate 8.2 and 16.1 improvements of our model on NDCG and MRR, respectively.

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Cited By

Li XXun YZhang Jchen XHou Y(2025)Sequence Recommendation Based on Hierarchical Filtering and Multi-contrastive LearningComputer Supported Cooperative Work and Social Computing10.1007/978-981-96-2376-1_25(340-351)Online publication date: 5-Mar-2025
https://doi.org/10.1007/978-981-96-2376-1_25
Liu YQi JYu Y(2025)A Review on Deep Learning for Sequential Recommender Systems: Key Technologies and DirectionsBig Data10.1007/978-981-96-1024-2_22(305-318)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-981-96-1024-2_22
Wen YChen STian YGuan WWang PDeng HXu JZheng BLi ZZou LLi CAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Unified Visual Preference Learning for User Intent UnderstandingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635858(816-825)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635858
Show More Cited By

Index Terms

Sequential Recommendation with Decomposed Item Feature Routing
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Collaborative filtering
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Publication History

Published: 25 April 2022

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Research-article
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Refereed limited

Funding Sources

Beijing Outstanding Young Scientist Program
Major Innovation & Planning Interdisciplinary Platform for the Double-First Class Initiative
National Natural Science Foundation of China
Public Computing Cloud, Renmin University of China
Intelligent Social Governance Platform
Renmin University of China

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WWW '22

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SIGWEB

WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Li XXun YZhang Jchen XHou Y(2025)Sequence Recommendation Based on Hierarchical Filtering and Multi-contrastive LearningComputer Supported Cooperative Work and Social Computing10.1007/978-981-96-2376-1_25(340-351)Online publication date: 5-Mar-2025
https://doi.org/10.1007/978-981-96-2376-1_25
Liu YQi JYu Y(2025)A Review on Deep Learning for Sequential Recommender Systems: Key Technologies and DirectionsBig Data10.1007/978-981-96-1024-2_22(305-318)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-981-96-1024-2_22
Wen YChen STian YGuan WWang PDeng HXu JZheng BLi ZZou LLi CAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Unified Visual Preference Learning for User Intent UnderstandingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635858(816-825)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635858
Zhou XLee K(2024)ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825602(690-699)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825602
Kang WJeong HLee SChoi S(2024)Independent Representation of Side Information for Sequential RecommendationIEEE Access10.1109/ACCESS.2024.347697612(148516-148524)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3476976
Liu XMeng SLi QQi LXu XDou WZhang XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)SMEF: Social-aware Multi-dimensional Edge Features-based Graph Representation Learning for RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615063(1566-1575)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615063
Hu HGuo WLiu YKan MFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Adaptive Multi-Modalities Fusion in Sequential Recommendation SystemsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614775(843-853)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614775

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