Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization
This addresses donation and channel recommendations for live multi-streaming platforms like Twitch and Douyu, representing an incremental advance in recommendation systems for this domain.
The paper tackles the problem of recommending live multi-streaming channels and donations by introducing a new formulation called DAMRec and proposing the MARS system, which improves hit ratio and mean average precision by at least 38.8% over existing methods.
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.