Social group based video recommendation addressing the cold-start problem

Chunfeng Yang, Yipeng Zhou, Liang Chen, Xiaopeng Zhang, Dah Ming Chiu

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

4 Citations (Scopus)

Abstract

Video recommendation has become an essential part of online video services. Cold start, a problem relatively common in the practical online video recommendation service, occurs when the user who needs video recommendation has no viewing history (Cold start consists of the new-user problem and the new-item problem. In this paper, we discuss the new-user one). A promising approach to resolve this problem is to capitalize on information in online social networks (OSNs): Videos viewed by a user’s friends may be good candidates for recommendation. However, in practice, this information is also quite limited, either because of insufficient friends or lack of abundant viewing history of friends. In this work, we utilize social groups with richer information to recommend videos. It is common that users may be affiliated with multiple groups in OSNs. Through members within the same group, we can reach a considerably larger set of users, hence more candidate videos for recommendation. In this paper, by collaborating with Tencent Video, we propose a social-group-based algorithm to produce personalized video recommendations by ranking candidate videos from the groups a user is affiliated with. This algorithm was implemented and tested in the Tencent Video service system. Compared with two state-of-the-art methods, the proposed algorithm not only improves the click-through rate, but also recommends more diverse videos.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication20th Pacific-Asia Conference, PAKDD 2016, Proceedings, Part II
EditorsJames Bailey, Latifur Khan, Takashi Washio, Gillian Dobbie, Joshua Zhexue Huang, Ruili Wang
Place of PublicationSwitzerland
PublisherSpringer, Springer Nature
Pages515-527
Number of pages13
ISBN (Electronic)9783319317502
ISBN (Print)9783319317496
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016 - Auckland, New Zealand
Duration: 19 Apr 201622 Apr 2016

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume9652
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016
CountryNew Zealand
CityAuckland
Period19/04/1622/04/16

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