It takes two to Tango: An exploration of domain pairs for cross-domain collaborative filtering

Sahebi, S and Brusilovsky, P (2015) It takes two to Tango: An exploration of domain pairs for cross-domain collaborative filtering. RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems. 131 - 138.

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Abstract

As the heterogeneity of data sources are increasing on the web, and due to the sparsity of data in each of these data sources, cross-domain recommendation is becoming an emerging research topic in the recent years. Cross-domain collaborative filtering aims to transfer the user rating pattern from source (auxiliary) domains to a target domain for the purpose of alleviating the sparsity problem and providing better target recommendations. However, the studies so far have either focused on a limited number of domains that are assumed to be related to each other (such as books and movies), or a division of the same dataset (such as movies) into different domains based on an item characteristic (such as genre). In this paper, we study a broad set of domains and their characteristics to understand the factors that affect the success or failure of cross-domain collaborative filtering, the amount of improvement in cross-domain approaches, and the selection of best source domains for a specific target domain. We propose to use Canonical Correlation Analysis (CCA) as a significant major factor in finding the most promising source domains for a target domain, and suggest a cross-domain collaborative filtering based on CCA (CDCCA) that proves to be successful in using the shared information between domains in the target recommendations.

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Details

Item Type:	Article
Status:	Published
Creators/Authors:	Creators Email Pitt Username ORCID Sahebi, S shs106@pitt.edu SHS106 Brusilovsky, P peterb@pitt.edu PETERB 0000-0002-1902-1464
Date:	16 September 2015
Date Type:	Publication
Journal or Publication Title:	RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems
Page Range:	131 - 138
Event Type:	Conference
DOI or Unique Handle:	10.1145/2792838.2800188
Schools and Programs:	Dietrich School of Arts and Sciences > Intelligent Systems School of Information Sciences > Information Science
Refereed:	Yes
ISBN:	9781450336925
Date Deposited:	05 Aug 2016 13:19
Last Modified:	30 Mar 2021 15:55
URI:	http://d-scholarship.pitt.edu/id/eprint/29119

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