research-article

Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation

Authors:

Zhuoxuan Jiang,

Dong-Dong Chen,

Jinfeng YiAuthors Info & Claims

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages 3886 - 3894

https://doi.org/10.1145/3447548.3467151

Published: 14 August 2021 Publication History

Abstract

Recently, a new form of online shopping becomes more and more popular, which combines live streaming with E-Commerce activity. The streamers introduce products and interact with their audiences, and hence greatly improve the performance of selling products. Despite of the successful applications in industries, the live stream E-commerce has not been well studied in the data science community. To fill this gap, we investigate this brand-new scenario and collect a real-world Live Stream E-Commerce (LSEC) dataset. Different from conventional E-commerce activities, the streamers play a pivotal role in the LSEC events. Hence, the key is to make full use of rich interaction information among streamers, users, and products. We first conduct data analysis on the tripartite interaction data and quantify the streamer's influence on users' purchase behavior. Based on the analysis results, we model the tripartite information as a heterogeneous graph, which can be decomposed to multiple bipartite graphs in order to better capture the influence. We propose a novel Live Stream E-Commerce Graph Neural Network framework (LSEC-GNN) to learn the node representations of each bipartite graph, and further design a multi-task learning approach to improve product recommendation. Extensive experiments on two real-world datasets with different scales show that our method can significantly outperform various baseline approaches.

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Deng JWang SWang YQi JZhao LZhou GMeng GBaeza-Yates RBonchi F(2024)MMBee: Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour ExpansionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671511(4896-4905)Online publication date: 25-Aug-2024
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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
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Index Terms

Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
2. Information systems
  1. World Wide Web

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cover image ACM Conferences

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

August 2021

4259 pages

ISBN:9781450383325

DOI:10.1145/3447548

General Chairs:
Feida Zhu
Singapore Management University
,
Beng Chin Ooi
National University of Singapore
,
Chunyan Miao
Nanyang Technology University
,
Program Chairs:
Haixun Wang,
Iryna Skrypnyk,
Wynne Hsu,
Sanjay Chawla

Copyright © 2021 ACM.

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Published: 14 August 2021

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August 14 - 18, 2021

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https://dl.acm.org/doi/10.1145/3637528.3671511
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
Rong MHan YZhang JGuo X(2024)A Behavior Relation-Aware Graph Contrastive Networks for Multi-Behavior Recommendation2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)10.1109/IMCEC59810.2024.10575070(1663-1670)Online publication date: 24-May-2024
https://doi.org/10.1109/IMCEC59810.2024.10575070
Lu YDuan Y(2024)Online content-based sequential recommendation considering multimodal contrastive representation and dynamic preferencesNeural Computing and Applications10.1007/s00521-024-09447-x36:13(7085-7103)Online publication date: 18-Feb-2024
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Gilstrap CGilstrap C(2023)Mobile Technologies and Live Streaming Commerce: A Systematic Review and Lexical Analysis2023 46th MIPRO ICT and Electronics Convention (MIPRO)10.23919/MIPRO57284.2023.10159883(36-44)Online publication date: 22-May-2023
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Mai XSheikh Ahmad FXu J(2023)A Comprehensive Bibliometric Analysis of Live Streaming Commerce: Mapping the Research LandscapeSage Open10.1177/2158244023121662013:4Online publication date: 19-Dec-2023
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Li HYu LNiu XLeng YDu Q(2023)Sequential and Graphical Cross-Domain Recommendations with a Multi-View Hierarchical Transfer GateACM Transactions on Knowledge Discovery from Data10.1145/360461518:1(1-28)Online publication date: 10-Aug-2023
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Raju ST. M RKumar SIslam S(2023)‘Watch and Buy’ and its impact on online retailers and manufacturer: A Stackelberg and Nash game analysis2023 4th International Conference on Data Analytics for Business and Industry (ICDABI)10.1109/ICDABI60145.2023.10629467(523-528)Online publication date: 25-Oct-2023
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