Social Attentive Network for Live Stream Recommendation

Dung Ru Yu, Chiao Chuan Chu, Hsu Chao Lai, Jiun Long Huang

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

Abstract

Live streaming platforms not only provide live videos but also allow social interactions between viewers via real-time chatting. However, none of existing research has studied the social impact for recommending live streams. In this work, we formulate a new personalized recommendation problem by factoring in both video and social contents (chats). Accordingly, we 1) design a new attention network ANSWER to identify viewers' attention on video and social contents, and 2) rank the channels based on the attentive features. We collect a real dataset from Twitch for evaluation. The experimental results manifest that ANSWER outperforms baselines by at least 26.6% in terms of NDCG@5.

Original languageEnglish
Title of host publicationThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
PublisherAssociation for Computing Machinery
Pages24-25
Number of pages2
ISBN (Electronic)9781450370240
DOIs
StatePublished - 20 Apr 2020
Event29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan
Duration: 20 Apr 202024 Apr 2020

Publication series

NameThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020

Conference

Conference29th International World Wide Web Conference, WWW 2020
CountryTaiwan
CityTaipei
Period20/04/2024/04/20

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