Active learning in multi-domain collaborative filtering recommender systems

Xin Guan, Chang Tsun Li, Yu Guan

Research output: Book chapter/Published conference paperConference paper

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.
Original languageEnglish
Title of host publicationProceedings of the 33rd Annual ACM Symposium on Applied Computing
PublisherAssociation for Computing Machinery
Pages1351-1357
Number of pages7
VolumePart F137816
ISBN (Electronic)9781450351911
DOIs
Publication statusPublished - 09 Apr 2018
Event33rd Annual ACM Symposium on Applied Computing, SAC 2018 - Palais Beaumont, Pau, France
Duration: 09 Apr 201813 Apr 2018
https://www.sigapp.org/sac/sac2018/ (Conference website)

Conference

Conference33rd Annual ACM Symposium on Applied Computing, SAC 2018
CountryFrance
CityPau
Period09/04/1813/04/18
Internet address

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  • Cite this

    Guan, X., Li, C. T., & Guan, Y. (2018). Active learning in multi-domain collaborative filtering recommender systems. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (Vol. Part F137816, pp. 1351-1357). Association for Computing Machinery. https://doi.org/10.1145/3167132.3167277