Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

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.

[img] Archive (ZIP)
Supplemental Material
Available under License : See the attached license file.

Download (73kB)
[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)

Abstract

© 2015 ACM. 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.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Sahebi, Sshs106@pitt.eduSHS106
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
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: 01 Nov 2017 12:57
URI: http://d-scholarship.pitt.edu/id/eprint/29119

Metrics

Monthly Views for the past 3 years

Plum Analytics

Altmetric.com


Actions (login required)

View Item View Item