Please use this identifier to cite or link to this item: https://doi.org/10.1145/2072298.2072055
Title: Integrating rich information for video recommendation with multi-task rank aggregation
Authors: Zhao, X.
Li, G. 
Wang, M. 
Yuan, J.
Zha, Z.-J. 
Li, Z.
Chua, T.-S. 
Keywords: Multi-task rank aggregation
Video recommendation
Issue Date: 2011
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
URI: http://scholarbank.nus.edu.sg/handle/10635/41730
ISBN: 9781450306164
DOI: 10.1145/2072298.2072055
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

14
checked on Jan 16, 2018

Page view(s)

50
checked on Jan 13, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.