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|Title:||Integrating rich information for video recommendation with multi-task rank aggregation|
|Keywords:||Multi-task rank aggregation|
|Source:||Zhao, X.,Li, G.,Wang, M.,Yuan, J.,Zha, Z.-J.,Li, Z.,Chua, T.-S. (2011). Integrating rich information for video recommendation with multi-task rank aggregation. MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops : 1521-1524. ScholarBank@NUS Repository. https://doi.org/10.1145/2072298.2072055|
|Abstract:||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 multitask 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. Copyright 2011 ACM.|
|Source Title:||MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops|
|Appears in Collections:||Staff Publications|
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