The Library
Active learning in multi-domain collaborative filtering recommender systems
Tools
Tools
Tools
Guan, Xin, Li, Chang-Tsun and Guan, Yu (2018) Active learning in multi-domain collaborative filtering recommender systems. In: 33rd Annual ACM Symposium on Applied Computing, SAC 2018, Pau, France, 9–13 April 2018. Published in: SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing pp. 1351-1357. doi:10.1145/3167132.3167277
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: http://dx.doi.org/10.1145/3167132.3167277
Abstract
The lack of information is an acute challenge in most recommender systems, especially for the collaborative filtering algorithms which utilize user-item rating matrix as the only source of information. Active learning can be used to remedy this problem by querying users to give ratings to some items. Apart from the active learning algorithms, cross-domain recommender system techniques try to alleviate the sparsity problem by exploiting knowledge from auxiliary (source) domains. A special case of cross-domain recommendation is multi-domain recommendation that utilizes the shared knowledge across multiple domains to alleviate the data sparsity in all domains. In this paper, we propose a novel multi-domain active learning framework by incorporating active learning techniques with cross-domain collaborative filtering algorithms in the multi-domain scenarios. Specifically, our proposed active learning elicits all the ratings simultaneously based on the criteria with regard to both items and users, for the purpose of improving the performance of the whole system. We evaluate a variety of active learning strategies in the proposed framework on different multi-domain recommendation tasks based on three popular datasets: Movielens, Netflix and Book-Crossing. The results show that the system performance can be improved further when combining cross-domain collaborative filtering with active learning algorithms.
| Item Type: | Conference Item (Paper) | ||||||
|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| Journal or Publication Title: | SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing | ||||||
| Publisher: | ACM | ||||||
| Book Title: | Proceedings of the 33rd Annual ACM Symposium on Applied Computing - SAC '18 | ||||||
| Official Date: | 13 April 2018 | ||||||
| Dates: |
|
||||||
| Page Range: | pp. 1351-1357 | ||||||
| DOI: | 10.1145/3167132.3167277 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | 33rd Annual ACM Symposium on Applied Computing, SAC 2018 | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Pau, France | ||||||
| Date(s) of Event: | 9–13 April 2018 |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
