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Confronting with the sheer amount of Over-the-Top (OTT) videos, personalized recommendation is especially important for users to locate videos of interest. However, previous approaches seldom considered the influence of watching time when designing video recommendation algorithms. In this paper, we first conduct a detailed measurement study on a leading OTT video service provider in China and our results show that user view preferences are substantially influenced by watching time. Based on the above results, we further propose a personalized time-aware video recommendation algorithm called TAMF for OTT videos. The basic idea of our proposed TAMF algorithm is to utilize matrix factorization to unveil how watching time affects user view interests and cluster time slots with similar influence. In this way, we can collaboratively learn users' personal interests if their views belong to the same cluster, and precisely capture user view preferences with watching time. Finally, we also conduct extensive experiments using real traces to evaluate the performance of our algorithm, and the experimental results show that our proposed algorithm can improve video recommendation performance by 4.83% and 4.42% in terms of WMRR and WMAP respectively and significantly boost user engagement.
|Title of host publication||Proceedings of the 29th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2019|
|Place of Publication||New York|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||6|
|Publication status||Published - 2019|
|Event||29th ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2019 - Amherst, United States|
Duration: 21 Jun 2019 → 21 Jun 2019
|Conference||29th ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2019|
|Period||21/06/19 → 21/06/19|
- Context-aware video recommendation
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