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Active learning in multi-domain collaborative filtering recommender systems
conference contribution
posted on 2018-01-01, 00:00 authored by X Guan, Chang-Tsun LiChang-Tsun Li, Y GuanThe 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.
History
Event
Association for Computing Machinery. Symposium (33rd : 2018 : Pau, France)Series
Association for Computing Machinery SymposiumPagination
1351 - 1357Publisher
Association for Computing MachineryLocation
Pau, FrancePlace of publication
New York, N.Y.Publisher DOI
Start date
2018-04-09End date
2018-04-13ISBN-13
9781450351911Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2018, the owner/author(s)Editor/Contributor(s)
[Unknown]Title of proceedings
SAC 2018 : Proceedings of the 33rd Annual ACM Symposium on Applied ComputingUsage metrics
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Keywords
recommender systemscollaborative filteringactive learningcross-domainmulti-domainScience & TechnologyTechnologyComputer Science, Theory & MethodsEngineering, Electrical & ElectronicComputer ScienceEngineeringcrossdomainInformation SystemsArtificial Intelligence and Image ProcessingDistributed Computing
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