TY - GEN
T1 - Social group based video recommendation addressing the cold-start problem
AU - Yang, Chunfeng
AU - Zhou, Yipeng
AU - Chen, Liang
AU - Zhang, Xiaopeng
AU - Chiu, Dah Ming
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84988001313&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-31750-2_41
DO - 10.1007/978-3-319-31750-2_41
M3 - Conference proceeding contribution
SN - 9783319317496
T3 - Lecture Notes in Artificial Intelligence
SP - 515
EP - 527
BT - Advances in Knowledge Discovery and Data Mining
A2 - Bailey, James
A2 - Khan, Latifur
A2 - Washio, Takashi
A2 - Dobbie, Gillian
A2 - Huang, Joshua Zhexue
A2 - Wang, Ruili
PB - Springer, Springer Nature
CY - Switzerland
T2 - 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016
Y2 - 19 April 2016 through 22 April 2016
ER -