research-article

ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation

Authors:

Zhiqiang Zhang,

Wenwu OuAuthors Info & Claims

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2231 - 2240

https://doi.org/10.1145/3397271.3401428

Published: 25 July 2020 Publication History

Abstract

Recommender system (RS) devotes to predicting user preference to a given item and has been widely deployed in most web-scale applications. Recently, knowledge graph (KG) attracts much attention in RS due to its abundant connective information. Existing methods either explore independent meta-paths for user-item pairs over KG, or employ graph neural network (GNN) on whole KG to produce representations for users and items separately. Despite effectiveness, the former type of methods fails to fully capture structural information implied in KG, while the latter ignores the mutual effect between target user and item during the embedding propagation. In this work, we propose a new framework named Adaptive Target-Behavior Relational Graph network (ATBRG for short) to effectively capture structural relations of target user-item pairs over KG. Specifically, to associate the given target item with user behaviors over KG, we propose the graph connect and graph prune techniques to construct adaptive target-behavior relational graph. To fully distill structural information from the sub-graph connected by rich relations in an end-to-end fashion, we elaborate on the model design of ATBRG, equipped with relation-aware extractor layer and representation activation layer. We perform extensive experiments on both industrial and benchmark datasets. Empirical results show that ATBRG consistently and significantly outperforms state-of-the-art methods. Moreover, ATBRG has also achieved a performance improvement of 5.1% on CTR metric after successful deployment in one popular recommendation scenario of Taobao APP.

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Cited By

Lin LChen XXia KWang SZhang DHe TSerra ESpezzano F(2024)Hierarchical Information Propagation and Aggregation in Disentangled Graph Networks for Audience ExpansionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680062(4702-4709)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680062
Huang BYu WXie RLuo JXiao JHuang J(2024)Lorentz equivariant model for knowledge-enhanced hyperbolic collaborative filteringKnowledge-Based Systems10.1016/j.knosys.2024.111590291(111590)Online publication date: May-2024
https://doi.org/10.1016/j.knosys.2024.111590
Lu JLi JLi WSong JXiao G(2024)Heterogeneous propagation graph convolution network for a recommendation system based on a knowledge graphEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109395138(109395)Online publication date: Dec-2024
https://doi.org/10.1016/j.engappai.2024.109395
Show More Cited By

Index Terms

ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2020

2548 pages

ISBN:9781450380164

DOI:10.1145/3397271

General Chairs:
Jimmy Huang
York University, Canada
,
Yi Chang
Jilin University, China
,
Xueqi Cheng
Chinese Academy of Sciences, China
,
Program Chairs:
Jaap Kamps
University of Amsterdam, Netherlands
,
Vanessa Murdock
Amazon, U.S.A.
,
Ji-Rong Wen
Renmin University of China, China
,
Yiqun Liu
Tsinghua University, China

Copyright © 2020 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Published: 25 July 2020

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SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval

July 25 - 30, 2020

Virtual Event, China

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Lin LChen XXia KWang SZhang DHe TSerra ESpezzano F(2024)Hierarchical Information Propagation and Aggregation in Disentangled Graph Networks for Audience ExpansionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680062(4702-4709)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680062
Huang BYu WXie RLuo JXiao JHuang J(2024)Lorentz equivariant model for knowledge-enhanced hyperbolic collaborative filteringKnowledge-Based Systems10.1016/j.knosys.2024.111590291(111590)Online publication date: May-2024
https://doi.org/10.1016/j.knosys.2024.111590
Lu JLi JLi WSong JXiao G(2024)Heterogeneous propagation graph convolution network for a recommendation system based on a knowledge graphEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109395138(109395)Online publication date: Dec-2024
https://doi.org/10.1016/j.engappai.2024.109395
Wang XZhang ZShen GLai SCheng YZhu S(2024)Multi-view knowledge graph convolutional networks for recommendationApplied Soft Computing10.1016/j.asoc.2024.112633(112633)Online publication date: Dec-2024
https://doi.org/10.1016/j.asoc.2024.112633
Shen XHuang YZheng LLiao XJin H(2024)A heterogeneous 3-D stacked PIM accelerator for GCN-based recommender systemsCCF Transactions on High Performance Computing10.1007/s42514-024-00180-46:2(150-163)Online publication date: 28-Feb-2024
https://doi.org/10.1007/s42514-024-00180-4
Liu ZChen SChen YSu JZhong JDong C(2024)A knowledge-enhanced interest segment division attention network for click-through rate predictionNeural Computing and Applications10.1007/s00521-024-10330-yOnline publication date: 17-Sep-2024
https://doi.org/10.1007/s00521-024-10330-y
Yang MJiang YZou S(2023)Knowledge Graph Convolutional Recommender Model with Collaborative Information and Common Neighbor Ranking Sampling2023 4th International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI)10.1109/ICHCI58871.2023.10278069(232-236)Online publication date: 4-Aug-2023
https://doi.org/10.1109/ICHCI58871.2023.10278069
Wang WHu BPeng ZZhong MZhang ZLiu ZZhang GZhou J(2023)GARCIA: Powering Representations of Long-tail Query with Multi–granularity Contrastive Learning2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00244(3182-3195)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00244
Wu YZhou J(2023)A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD57460.2023.10152706(1520-1525)Online publication date: 24-May-2023
https://doi.org/10.1109/CSCWD57460.2023.10152706
Yan SWang HLi YWu CHan LShi CGuo R(2023)Metapath-guided dual semantic-aware filtering for HIN-based recommendationThe Journal of Supercomputing10.1007/s11227-023-05113-679:11(11934-11964)Online publication date: 4-Mar-2023
https://doi.org/10.1007/s11227-023-05113-6
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