Regularized multi--task learning
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MM '08 Proceedings of the 16th ACM international conference on Multimedia
MM '09 Proceedings of the 17th ACM international conference on Multimedia
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IEEE Transactions on Circuits and Systems for Video Technology
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Proceedings of the 20th ACM international conference on Information and knowledge management
Ranking Model Adaptation for Domain-Specific Search
IEEE Transactions on Knowledge and Data Engineering
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IEEE Transactions on Multimedia
Exploiting social relations for sentiment analysis in microblogging
Proceedings of the sixth ACM international conference on Web search and data mining
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Video recommendation is an important approach for helping people to access interesting videos. In this paper, we propose a scheme to integrate rich information for video recommendation. We regard video recommendation as a ranking problem and generate multiple ranking lists by exploring different information sources. A multi-task rank aggregation approach is proposed to integrate the ranking lists for different users in a joint manner. Our scheme is flexible and can easily incorporate other methods by adding their generated ranking lists into our multi-task learning algorithm. We conduct experiments with 76 users and more than 10,000 videos. The results demonstrate the feasibility and effectiveness of our approach.