Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation-Reference-Cited by-同舟云学术

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

Published:2021-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
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Author:

Yu Sanshi¹,Jiang Zhuoxuan²,Chen Dong-Dong³,Feng Shanshan⁴,Li Dongsheng⁵,Liu Qi¹,Yi Jinfeng³

Affiliation:

1. University of Science and Technology of China, Hefei, China

2. Tencent, Shanghai, China

3. JD AI Research, Beijing, China

4. Harbin Institute of Technology, Shenzhen, Shenzhen, China

5. Microsoft Research Asia, Shanghai, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3447548.3467151

Reference44 articles.

1. Everyone Can Be a Star: Quantifying Grassroots Online Sellers’ Live Streaming Effects on Product Sales

2. Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

3. Graph Neural Networks for Social Recommendation

4. Lauren Hallanan. 2020. Live Streaming Drives 6 Billion USD In Sales During The 11.11 Global Shopping Festival. https://www.forbes.com/sites/laurenhallanan/2020/11/16/live-streaming-drives-6-billion-usd-in-sales-during-the-1111-global-shopping-festival/'sh=6671147c21e5 Retrieved February 7 2021 from Lauren Hallanan. 2020. Live Streaming Drives 6 Billion USD In Sales During The 11.11 Global Shopping Festival. https://www.forbes.com/sites/laurenhallanan/2020/11/16/live-streaming-drives-6-billion-usd-in-sales-during-the-1111-global-shopping-festival/'sh=6671147c21e5 Retrieved February 7 2021 from

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