Name
Title | ||
|---|---|---|
Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization |
Abstract | ||
|---|---|---|
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.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3340531.3411925 | CIKM '20: The 29th ACM International Conference on Information and Knowledge Management
Virtual Event
Ireland
October, 2020 |
DocType | ISSN | ISBN |
Conference | Proceedings of the 29th ACM International Conference on
Information & Knowledge Management 1 2020 665-674 | 978-1-4503-6859-9 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsu-chao Lai | 1 | 2 | 3.74 |
| Jui-Yi Tsai | 2 | 0 | 0.34 |
| Hong-Han Shuai | 3 | 100 | 24.80 |
| Jiun-Long Huang | 4 | 592 | 47.09 |
| Wang-Chien Lee | 5 | 5765 | 346.32 |
| De-Nian Yang | 6 | 586 | 66.66 |