research-article

Social Attentive Network for Live Stream Recommendation

Authors:

Dung-Ru Yu,

Chiao-Chuan Chu,

Hsu-Chao Lai,

Jiun-Long HuangAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 24 - 25

https://doi.org/10.1145/3366424.3382679

Published: 20 April 2020 Publication History

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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.

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Y. Deldjoo 2016. Using visual features and latent factors for movie recommendation. In ACM RecSys.

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Q. V. Le and T. Mikolov. 2014. Distributed Representations of Sentences and Documents. In IEEE ICML. 1188–1196.

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S. Rendle 2009. Bpr: bayesian personalized ranking from implicit feedback. In UAI.

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P. Sun 2018. Attentive recurrent social recommendation. In ACM SIGIR.

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D. Y. Wohn 2018. Explaining viewers’ emotional, instrumental, and financial support provision for live streamers. In ACM CHI.

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Index Terms

Social Attentive Network for Live Stream Recommendation
1. Information systems

Index terms have been assigned to the content through auto-classification.

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Published In

WWW '20: Companion Proceedings of the Web Conference 2020

April 2020

854 pages

ISBN:9781450370240

DOI:10.1145/3366424

Editors:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2020

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SIGWEB

WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Abstract

References

Index Terms

Recommendations

Attentive Recurrent Social Recommendation

Social Influence Attentive Neural Network for Friend-Enhanced Recommendation

Multi-head Attentive Social Recommendation

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