Please use this identifier to cite or link to this item: https://doi.org/10.1145/2382336.2382382
Title: Personalized video recommendation based on viewing history with the study on YouTube
Authors: Zhao, X.
Luan, H. 
Cai, J.
Yuan, J.
Chen, X.
Li, Z.
Keywords: Multimodal similarity
Personalized video recommendation
Tag set
Viewing history
Issue Date: 2012
Source: Zhao, X.,Luan, H.,Cai, J.,Yuan, J.,Chen, X.,Li, Z. (2012). Personalized video recommendation based on viewing history with the study on YouTube. ACM International Conference Proceeding Series : 161-165. ScholarBank@NUS Repository. https://doi.org/10.1145/2382336.2382382
Abstract: 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. Copyright © 2012 ACM.
Source Title: ACM International Conference Proceeding Series
URI: http://scholarbank.nus.edu.sg/handle/10635/41891
ISBN: 9781450316002
DOI: 10.1145/2382336.2382382
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