@inproceedings{13299bc2635b4c51a7d96ceb03e8f54c,
title = "Integrating rich information for video recommendation with multi-task rank aggregation",
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.",
keywords = "Multi-task rank aggregation, Video recommendation",
author = "Xiaojian Zhao and Guangda Li and Meng Wang and Jin Yuan and Zha, \{Zheng Jun\} and Zhoujun Li and Chua, \{Tat Seng\}",
year = "2011",
doi = "10.1145/2072298.2072055",
language = "英语",
isbn = "9781450306164",
series = "MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops",
pages = "1521--1524",
booktitle = "MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops",
note = "19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11 ; Conference date: 28-11-2011 Through 01-12-2011",
}