TY - GEN
T1 - Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization
AU - Lai, Hsu Chao
AU - Tsai, Jui Yi
AU - Shuai, Hong Han
AU - Huang, Jiun Long
AU - Lee, Wang Chien
AU - Yang, De Nian
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/10/19
Y1 - 2020/10/19
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85095863602&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85095863602&partnerID=8YFLogxK
U2 - 10.1145/3340531.3411925
DO - 10.1145/3340531.3411925
M3 - Conference contribution
AN - SCOPUS:85095863602
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 665
EP - 674
BT - CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 29th ACM International Conference on Information and Knowledge Management, CIKM 2020
Y2 - 19 October 2020 through 23 October 2020
ER -