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Open access

Coarse-to-Fine Sparse Sequential Recommendation

Authors:

Christos Faloutsos,

George Karypis,

Soo-Min Pantel,

Julian McAuleyAuthors Info & Claims

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

Pages 2082 - 2086

https://doi.org/10.1145/3477495.3531732

Published: 07 July 2022 Publication History

Abstract

Sequential recommendation aims to model dynamic user behavior from historical interactions. Self-attentive methods have proven effective at capturing short-term dynamics and long-term preferences. Despite their success, these approaches still struggle to model sparse data, on which they struggle to learn high-quality item representations. We propose to model user dynamics from shopping intents and interacted items simultaneously. The learned intents are coarse-grained and work as prior knowledge for item recommendation. To this end, we present a coarse-to-fine self-attention framework, namely CaFe, which explicitly learns coarse-grained and fine-grained sequential dynamics. Specifically, CaFe first learns intents from coarse-grained sequences which are dense and hence provide high-quality user intent representations. Then, CaFe fuses intent representations into item encoder outputs to obtain improved item representations. Finally, we infer recommended items based on representations of items and corresponding intents. Experiments on sparse datasets show that CaFe outperforms state-of-the-art self-attentive recommenders by 44.03% NDCG@5 on average.

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MP4 File (SIGIR22-sp1478.mp4)

The video for our SIGIR 2022 short paper "Coarse-to-Fine Sparse Sequential Recommendation". The video briefly introduce our motivating experiments; model structure; training and inference; experimental results in this paper.

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Cited By

Shi LYang JLv PYuan LKou FLuo JXu MCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Self-derived Knowledge Graph Contrastive Learning for RecommendationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681693(7571-7580)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681693
Liu YWalder CXie LLiu YBaeza-Yates RBonchi F(2024)Probabilistic Attention for Sequential RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671733(1956-1967)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671733
Yue ZWang YHe ZZeng HMcauley JWang DAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Linear Recurrent Units for Sequential RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635760(930-938)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635760
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Index Terms

Coarse-to-Fine Sparse Sequential Recommendation
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Personalization

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Published In

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

Copyright © 2022 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

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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

Shi LYang JLv PYuan LKou FLuo JXu MCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Self-derived Knowledge Graph Contrastive Learning for RecommendationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681693(7571-7580)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681693
Liu YWalder CXie LLiu YBaeza-Yates RBonchi F(2024)Probabilistic Attention for Sequential RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671733(1956-1967)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671733
Yue ZWang YHe ZZeng HMcauley JWang DAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Linear Recurrent Units for Sequential RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635760(930-938)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635760
Wang CWang MWang XTan Y(2024)IPSRM: An intent perceived sequential recommendation modelJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10220636:9(102206)Online publication date: Nov-2024
https://doi.org/10.1016/j.jksuci.2024.102206
Zhang YYang BMao RLi Q(2024)MGTExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121808238:PBOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121808
Li FPeng HYu SZeng FChen FYang F(2024)Revisiting the loss functions in sequential recommendationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109366138(109366)Online publication date: Dec-2024
https://doi.org/10.1016/j.engappai.2024.109366
Su ZZhang JFang ZGao Y(2024)Enhanced side information fusion framework for sequential recommendationInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02328-8Online publication date: 10-Sep-2024
https://doi.org/10.1007/s13042-024-02328-8
Zeng BShi HLi YLi RDeng H(2024)Leveraging Large Language Models Knowledge Enhancement Dual-Stage Fine-Tuning Framework for RecommendationNatural Language Processing and Chinese Computing10.1007/978-981-97-9434-8_26(333-345)Online publication date: 1-Nov-2024
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Li JWang MLi JFu JShen XShang JMcAuley JSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Text Is All You Need: Learning Language Representations for Sequential RecommendationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599519(1258-1267)Online publication date: 6-Aug-2023
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