TAMF: towards personalized time-aware recommendation for Over-the-Top videos

Zhanpeng Wu, Yipeng Zhou, Di Wu*, Min Chen, Yuedong Xu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 29th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2019
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages43-48
Number of pages6
ISBN (Electronic)9781450362993
DOIs
Publication statusPublished - 2019
Event29th ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2019 - Amherst, United States
Duration: 21 Jun 2019 → …

Conference

Conference29th ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2019
CountryUnited States
CityAmherst
Period21/06/19 → …

Keywords

  • Clustering
  • Context-aware video recommendation
  • Time-aware

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