short-paper

Denoising Time Cycle Modeling for Recommendation

Authors:

Wenliang ZhongAuthors Info & Claims

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

Pages 1950 - 1955

https://doi.org/10.1145/3477495.3531785

Published: 07 July 2022 Publication History

Abstract

Recently, modeling temporal patterns of user-item interactions have attracted much attention in recommender systems. We argue that existing methods ignore the variety of temporal patterns of user behaviors. We define the subset of user behaviors that are ir- relevant to the target item as noises, which limits the performance of target-related time cycle modeling and affect the recommendation performance. In this paper, we propose Denoising Time Cycle Modeling (DiCycle), a novel approach to denoise user behaviors and select the subset of user behaviors that are highly related to the target item. DiCycle is able to explicitly model diverse time cycle patterns for recommendation. Extensive experiments are conducted on both public benchmarks and a real-world dataset, demonstrating the superior performance of DiCycle over the state-of-the-art recommendation methods.

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Cited By

Zhou HPan JZhou XChen XJiang JGao XChen GChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Temporal Interest Network for User Response PredictionCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648340(413-422)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648340
Wei QLiu XXian DXu JLiu LLong S(2024)UICF: a new user-item composite filtering recommendation framework by leveraging temporal semanticsInternational Journal of Intelligent Computing and Cybernetics10.1108/IJICC-01-2024-001617:3(577-604)Online publication date: 24-Jun-2024
https://doi.org/10.1108/IJICC-01-2024-0016
Wang PLi CZou LFeng ZLi KLi XLiu XWang SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Tutorial: Data Denoising Metrics in Recommender SystemsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615297(5224-5227)Online publication date: 21-Oct-2023
https://doi.org/10.1145/3583780.3615297
Show More Cited By

Index Terms

Denoising Time Cycle Modeling for Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      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.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2022

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SIGIR '22

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SIGIR

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

Zhou HPan JZhou XChen XJiang JGao XChen GChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Temporal Interest Network for User Response PredictionCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648340(413-422)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648340
Wei QLiu XXian DXu JLiu LLong S(2024)UICF: a new user-item composite filtering recommendation framework by leveraging temporal semanticsInternational Journal of Intelligent Computing and Cybernetics10.1108/IJICC-01-2024-001617:3(577-604)Online publication date: 24-Jun-2024
https://doi.org/10.1108/IJICC-01-2024-0016
Wang PLi CZou LFeng ZLi KLi XLiu XWang SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Tutorial: Data Denoising Metrics in Recommender SystemsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615297(5224-5227)Online publication date: 21-Oct-2023
https://doi.org/10.1145/3583780.3615297
Li JZhang YLin XYang XZhou GLi LChen HZhou JChen HDuh WHuang HKato MMothe JPoblete B(2023)TAML: Time-Aware Meta Learning for Cold-Start Problem in News RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592068(2415-2419)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592068

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