  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.
