TY - GEN
T1 - On video recommendation over social network
AU - Zhao, Xiaojian
AU - Yuan, Jin
AU - Hong, Richang
AU - Wang, Meng
AU - Li, Zhoujun
AU - Chua, Tat Seng
PY - 2012
Y1 - 2012
N2 - Video recommendation is a hot research topic to help people access interesting videos. The existing video recommendation approaches include CBF, CF and HF. However, these approaches treat the relationships between all users as equal and neglect an important fact that the acquaintances or friends may be a more reliable source than strangers to recommend interesting videos. Thus, in this paper we propose a novel approach to improve the accuracy of 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 relationship strengths between the user and his friends. The final recommended videos are ranked according to the accumulated recommendation scores from different recommenders. We conducted experiments with 45 participants and the results demonstrated the feasibility and effectiveness of our approach.
AB - Video recommendation is a hot research topic to help people access interesting videos. The existing video recommendation approaches include CBF, CF and HF. However, these approaches treat the relationships between all users as equal and neglect an important fact that the acquaintances or friends may be a more reliable source than strangers to recommend interesting videos. Thus, in this paper we propose a novel approach to improve the accuracy of 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 relationship strengths between the user and his friends. The final recommended videos are ranked according to the accumulated recommendation scores from different recommenders. We conducted experiments with 45 participants and the results demonstrated the feasibility and effectiveness of our approach.
KW - Activity Domain
KW - Relationship Strength
KW - Video Recommendation
UR - https://www.scopus.com/pages/publications/84862916433
U2 - 10.1007/978-3-642-27355-1_16
DO - 10.1007/978-3-642-27355-1_16
M3 - 会议稿件
AN - SCOPUS:84862916433
SN - 9783642273544
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 149
EP - 160
BT - Advances in Multimedia Modeling - 18th International Conference, MMM 2012, Proceedings
T2 - 18th International Conference on Multimedia Modeling, MMM 2012
Y2 - 4 January 2012 through 6 January 2012
ER -