Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization

Hsu Chao Lai, Jui Yi Tsai, Hong Han Shuai, Jiun Long Huang, Wang Chien Lee, De Nian Yang

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

In contrast to traditional online videos, live multi-streaming supports real-time social interactions between multiple streamers and viewers, such as donations. However, donation and multi-streaming channel recommendations are challenging due to complicated streamer and viewer relations, asymmetric communications, and the tradeoff between personal interests and group interactions. In this paper, we introduce Multi-Stream Party (MSP) and formulate a new multi-streaming recommendation problem, called Donation and MSP Recommendation (DAMRec). We propose Multi-stream Party Recommender System (MARS) to extract latent features via socio-temporal coupled donation-response tensor factorization for donation and MSP recommendations. Experimental results on Twitch and Douyu manifest that MARS significantly outperforms existing recommenders by at least 38.8% in terms of hit ratio and mean average precision.

原文English
主出版物標題CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
發行者Association for Computing Machinery
頁面665-674
頁數10
ISBN(電子)9781450368599
DOIs
出版狀態Published - 19 10月 2020
事件29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
持續時間: 19 10月 202023 10月 2020

出版系列

名字International Conference on Information and Knowledge Management, Proceedings

Conference

Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
國家/地區Ireland
城市Virtual, Online
期間19/10/2023/10/20

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