mlsanityData to 7 Oct 2026

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

arXiv 2106.03415 ↗First posted 7 Jun 2021cs.IR cs.AIcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 23 of its 45 references that are in the map. First posted on arXiv 7 Jun 2021.

Builds on

23 of 45 references.

References · 23

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11 more
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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 3; 2025: 2.

7 papers.

  1. 2021Multi-Sample based Contrastive Loss for Top-k Recommendationcited by 7
  2. 2025KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendationcited by 5
  3. 2024Ensure Timeliness and Accuracy: A Novel Sliding Window Data Stream Paradigm for Live Streaming Recommendationcited by 4
  4. 2024Exploring Large Language Models for Product Attribute Value Identificationcited by 3
  5. 2023ContentCTR: Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformercited by 2
  6. 2024MMBee: Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour Expansioncited by 2
  7. 2025Enhancing Live Broadcast Engagement: A Multi-modal Approach to Short Video Recommendations Using MMGCN and User Preferencescited by 0