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Compressed Interaction Graph based Framework for Multi-behavior Recommendation

Authors:

Rui ZhangAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 960 - 970

https://doi.org/10.1145/3543507.3583312

Published: 30 April 2023 Publication History

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Abstract

Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users’ multi-faceted preferences. However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data “as features” and gradient conflict in multi-task learning when treating multi-behavior data “as labels”. In this paper, we propose CIGF, a Compressed Interaction Graph based Framework, to overcome the above limitations. Specifically, we design a novel Compressed Interaction Graph Convolution Network (CIGCN) to model instance-level high-order relations explicitly. To alleviate the potential gradient conflict when treating multi-behavior data “as labels”, we propose a Multi-Expert with Separate Input (MESI) network with separate input on the top of CIGCN for multi-task learning. Comprehensive experiments on three large-scale real-world datasets demonstrate the superiority of CIGF.

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Cited By

Yang YZhu QChen JLarson K(2024)VCformerProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/590(5335-5343)Online publication date: 3-Aug-2024
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Chen ZZhang FChen YFang XFeng GZhu XChen WDu X(2024)Enabling Window-Based Monotonic Graph Analytics with Reusable Transitional Results for Pattern-Consistent QueriesProceedings of the VLDB Endowment10.14778/3681954.368197917:11(3003-3016)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3681979
Park GYang HYeom KMyeongheon JKo YOh BLee K(2024)Knowledge-constrained interest-aware multi-behavior recommendation with behavior pattern identificationInformation Sciences10.1016/j.ins.2024.121652(121652)Online publication date: Nov-2024
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Index Terms

Compressed Interaction Graph based Framework for Multi-behavior Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

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Published: 30 April 2023

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Science and Technology Innovation 2030-Key Project

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WWW '23

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WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

TX, Austin, USA

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

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Cited By

Yang YZhu QChen JLarson K(2024)VCformerProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/590(5335-5343)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/590
Chen ZZhang FChen YFang XFeng GZhu XChen WDu X(2024)Enabling Window-Based Monotonic Graph Analytics with Reusable Transitional Results for Pattern-Consistent QueriesProceedings of the VLDB Endowment10.14778/3681954.368197917:11(3003-3016)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3681979
Park GYang HYeom KMyeongheon JKo YOh BLee K(2024)Knowledge-constrained interest-aware multi-behavior recommendation with behavior pattern identificationInformation Sciences10.1016/j.ins.2024.121652(121652)Online publication date: Nov-2024
https://doi.org/10.1016/j.ins.2024.121652
Zhang ZJia YHou YYu X(2024)Explicit Behavior Interaction with Heterogeneous Graph for Multi-behavior RecommendationData Science and Engineering10.1007/s41019-023-00238-39:2(133-151)Online publication date: 19-Jan-2024
https://doi.org/10.1007/s41019-023-00238-3
Yang BGuo XShang LZhang ZBi YGu X(2024)Multi-behavior Recommendation with Hypergraph Contrastive LearningImage and Graphics Technologies and Applications10.1007/978-981-97-9919-0_19(233-246)Online publication date: 22-Dec-2024
https://doi.org/10.1007/978-981-97-9919-0_19
Wu XWang SZhang Y(2024)Adaptive Augmentation and Neighbor Contrastive Learning for Multi-Behavior RecommendationWeb and Big Data10.1007/978-981-97-7235-3_2(18-32)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7235-3_2
Meng CZhao ZGuo WZhang YWu HGao CLi DLi XTang R(2023)Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior RecommendationACM Transactions on Information Systems10.1145/360636942:1(1-27)Online publication date: 18-Aug-2023
https://dl.acm.org/doi/10.1145/3606369
Liu WGuo WLiu YTang RWang H(2023)User Behavior Modeling with Deep Learning for Recommendation: Recent AdvancesProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3609496(1286-1287)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3609496
Meng CZhai CYang YZhang HLi XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Parallel Knowledge Enhancement based Framework for Multi-behavior RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615004(1797-1806)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615004
Yu CWang FShao ZSun TWu LXu YFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term PredictionProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614851(3062-3072)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614851
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