Cross-domain recommendation for large-scale data

Sahebi, S and Brusilovsky, P and Bobrokov, V (2017) Cross-domain recommendation for large-scale data. In: UNSPECIFIED.

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Abstract

Cross-domain algorithms have been introduced to help improving recommendations and to alleviate cold-start problem, especially in small and sparse datasets. These algorithms work by transferring information from source domain(s) to target domain. In this paper, we study if such algorithms can be helpful for large-scale datasets. We introduce a large-scale cross-domain recommender algorithm derived from canonical correlation analysis and analyze its performance, in comparison with single and cross-domain baseline algorithms. Our experiments in both cold-start and hot-start situations show the effectiveness of the proposed approach.

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Details

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Status:	Published
Creators/Authors:	Creators Email Pitt Username ORCID Sahebi, S Brusilovsky, P peterb@pitt.edu PETERB 0000-0002-1902-1464 Bobrokov, V
Date:	1 January 2017
Date Type:	Publication
Journal or Publication Title:	CEUR Workshop Proceedings
Volume:	1887
Page Range:	9 - 15
Event Type:	Conference
Schools and Programs:	School of Computing and Information > Information Science
ISSN:	1613-0073
Date Deposited:	12 Dec 2018 19:04
Last Modified:	27 Mar 2021 10:55
URI:	http://d-scholarship.pitt.edu/id/eprint/35050

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