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In contrast to traditional online videos, live multi-streaming supportsreal-time social interactions between multiple streamers and viewers, such asdonations. However, donation and multi-streaming channel recommendations arechallenging due to complicated streamer and viewer relations, asymmetriccommunications, and the tradeoff between personal interests and groupinteractions. In this paper, we introduce Multi-Stream Party (MSP) andformulate a new multi-streaming recommendation problem, called Donation and MSPRecommendation (DAMRec). We propose Multi-stream Party Recommender System(MARS) to extract latent features via socio-temporal coupled donation-responsetensor factorization for donation and MSP recommendations. Experimental resultson Twitch and Douyu manifest that MARS significantly outperforms existingrecommenders by at least 38.8% in terms of hit ratio and mean averageprecision.

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