Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization
Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization
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DOI：
10.1145/3340531.3411925
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Hsu-Chao Lai;Jui-Yi Tsai;Hong-Han Shuai;Jiun-Long Huang;Wang-Chien Lee;De-Nian Yang
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文献类型：
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作者：
Hsu-Chao Lai;Jui-Yi Tsai;Hong-Han Shuai;Jiun-Long Huang;Wang-Chien Lee;De-Nian Yang
与传统的在线视频相比，直播多流媒体支持多个主播和观众之间的实时社交互动，例如捐赠。然而，捐赠和多流媒体频道推荐是具有挑战性的，由于复杂的流媒体和观众的关系，不对称的通信，以及个人兴趣和群体互动之间的权衡。在本文中，我们引入多流党（MSP），并制定了一个新的多流推荐问题，称为捐赠和MSP推荐（DAMRec）。我们提出了多流党推荐系统（MARS）通过社会时间耦合捐赠-响应张量因子分解捐赠和MSP建议提取潜在的功能。在Twitch和斗鱼上的实验结果表明，MARS在命中率和平均精度方面明显优于现有的搜索引擎至少38.8%。
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.