research-article

Multi-Behavior Recommendation with Cascading Graph Convolution Networks

Authors:

Yuxin PengAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

Pages 1181 - 1189

https://doi.org/10.1145/3543507.3583439

Published: 30 April 2023 Publication History

Abstract

Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users’ potential interactions on the target behavior (e.g., buy), is regarded as an effective way to alleviate the data sparsity or cold-start issues in recommendation. Multi-behaviors are often taken in certain orders in real-world applications (e.g., click>cart>buy). In a behavior chain, a latter behavior usually exhibits a stronger signal of user preference than the former one does. Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning. In this work, we propose a novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN). In MB-CGCN, the embeddings learned from one behavior are used as the input features for the next behavior’s embedding learning after a feature transformation operation. In this way, our model explicitly utilizes the behavior dependencies in embedding learning. Experiments on two benchmark datasets demonstrate the effectiveness of our model on exploiting multi-behavior data. It outperforms the best baseline by 33.7% and 35.9% on average over the two datasets in terms of Recall@10 and NDCG@10, respectively.

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Cited By

Purificato EBoratto LDe Luca E(2024)Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging TrendsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3653062(13-16)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3653062
Gu PHu HXu G(2024)Modeling multi-behavior sequence via HyperGRU contrastive network for micro-video recommendationKnowledge-Based Systems10.1016/j.knosys.2024.111841295(111841)Online publication date: Jul-2024
https://doi.org/10.1016/j.knosys.2024.111841
Zhang YCheng ZLiu FYang XPeng Y(2024)Decoupled domain-specific and domain-conditional representation learning for cross-domain recommendationInformation Processing & Management10.1016/j.ipm.2024.10368961:3(103689)Online publication date: May-2024
https://doi.org/10.1016/j.ipm.2024.103689
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Index Terms

Multi-Behavior Recommendation with Cascading Graph Convolution Networks
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Personalization
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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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 ACM.

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Published: 30 April 2023

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the National Key R&D Program of China
the National Natural Science Foundation of China

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WWW '23

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SIGWEB

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

Purificato EBoratto LDe Luca E(2024)Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging TrendsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3653062(13-16)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3653062
Gu PHu HXu G(2024)Modeling multi-behavior sequence via HyperGRU contrastive network for micro-video recommendationKnowledge-Based Systems10.1016/j.knosys.2024.111841295(111841)Online publication date: Jul-2024
https://doi.org/10.1016/j.knosys.2024.111841
Zhang YCheng ZLiu FYang XPeng Y(2024)Decoupled domain-specific and domain-conditional representation learning for cross-domain recommendationInformation Processing & Management10.1016/j.ipm.2024.10368961:3(103689)Online publication date: May-2024
https://doi.org/10.1016/j.ipm.2024.103689
Deng JWu QWang SYe JWang PDu M(2024)A novel joint neural collaborative filtering incorporating rating reliabilityInformation Sciences10.1016/j.ins.2024.120406665(120406)Online publication date: Apr-2024
https://doi.org/10.1016/j.ins.2024.120406
Zhang ZJia YHou YYu X(2024)Explicit Behavior Interaction with Heterogeneous Graph for Multi-behavior RecommendationData Science and Engineering10.1007/s41019-023-00238-3Online publication date: 19-Jan-2024
https://doi.org/10.1007/s41019-023-00238-3
Elsayed SRashed ASchmidt-Thieme L(2024)HMAR: Hierarchical Masked Attention for Multi-behaviour RecommendationAdvances in Knowledge Discovery and Data Mining10.1007/978-981-97-2262-4_11(131-143)Online publication date: 25-Apr-2024
https://doi.org/10.1007/978-981-97-2262-4_11
Saito YSugiyama K(2023)Multi-Behavior Job Recommendation with Dynamic AvailabilityProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625314(264-271)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625314
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
Qin YGao CWei SWang YJin DYuan JZhang LLi DHao JLi Y(2023)Learning from Hierarchical Structure of Knowledge Graph for RecommendationACM Transactions on Information Systems10.1145/359563242:1(1-24)Online publication date: 21-Aug-2023
https://dl.acm.org/doi/10.1145/3595632
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
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