TY - GEN
T1 - Personalized video recommendation based on viewing history with the study on YouTube
AU - Zhao, Xiaojian
AU - Luan, Huanbo
AU - Cai, Junjie
AU - Yuan, Jin
AU - Chen, Xiaoming
AU - Li, Zhoujun
PY - 2012
Y1 - 2012
N2 - With internet delivery of video content surging to an un-precedented level, video recommendation has become an important approach for helping people access interesting videos. In this paper, we propose a novel approach to integrate viewing history for personalized video recommendation. For a given user, our approach calculates a recommendation score for each video candidate, that composes of two parts: the interest degree of this video by the user's friends, and the taste similarities between the user and his friends. We measure the interest degree of each video by considering its textual, visual and popularity information. Meanwhile, we construct tag set for each user based on his/her viewing history to estimate the taste similarities between different users. The final recommended videos are ranked according to the accumulated recommendation scores from different recommenders. We conduct experiments with 45 users and more than 11, 000 videos, and the results demonstrate the feasibility and effectiveness of our approach.
AB - With internet delivery of video content surging to an un-precedented level, video recommendation has become an important approach for helping people access interesting videos. In this paper, we propose a novel approach to integrate viewing history for personalized video recommendation. For a given user, our approach calculates a recommendation score for each video candidate, that composes of two parts: the interest degree of this video by the user's friends, and the taste similarities between the user and his friends. We measure the interest degree of each video by considering its textual, visual and popularity information. Meanwhile, we construct tag set for each user based on his/her viewing history to estimate the taste similarities between different users. The final recommended videos are ranked according to the accumulated recommendation scores from different recommenders. We conduct experiments with 45 users and more than 11, 000 videos, and the results demonstrate the feasibility and effectiveness of our approach.
KW - Multimodal similarity
KW - Personalized video recommendation
KW - Tag set
KW - Viewing history
UR - https://www.scopus.com/pages/publications/84869039735
U2 - 10.1145/2382336.2382382
DO - 10.1145/2382336.2382382
M3 - 会议稿件
AN - SCOPUS:84869039735
SN - 9781450316002
T3 - ACM International Conference Proceeding Series
SP - 161
EP - 165
BT - ICIMCS 2012 - Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
T2 - 4th International Conference on Internet Multimedia Computing and Service, ICIMCS 2012
Y2 - 9 September 2012 through 11 September 2012
ER -