Tags as Bridges between Domains: Improving Recommendation with Tag-Induced Cross-Domain Collaborative Filtering User Modeling, Adaption and Personalization
Y. Shi, M. Larson, and A. Hanjalic. User Modeling, Adaption and Personalization, volume 6787 of Lecture Notes in Computer Science, chapter 26, Springer Berlin / Heidelberg, Berlin, Heidelberg, (2011)
DOI: 10.1007/978-3-642-22362-4_26
Abstract
Recommender systems generally face the challenge of making predictions using only the relatively few user ratings available for a given domain. Cross-domain collaborative filtering (CF) aims to alleviate the effects of this data sparseness by transferring knowledge from other domains. We propose a novel algorithm, Tag-induced Cross-Domain Collaborative Filtering (TagCDCF), which exploits user-contributed tags that are common to multiple domains in order to establish the cross-domain links necessary for successful cross-domain CF. TagCDCF extends the state-of-the-art matrix factorization by introducing a constraint involving tag-based similarities between pairs of users and pairs of items across domains. The method requires no common users or items across domains. Using two publicly available CF data sets as different domains, we experimentally demonstrate that TagCDCF substantially outperforms other state-of-the-art single domain CF and cross-domain CF approaches. Additional experiments show that TagCDCF addresses data sparseness and illustrate the influence of the number of tags used by users in both domains.
%0 Book Section
%1 brusilovsky:Shi2011CrossDomain
%A Shi, Yue
%A Larson, Martha
%A Hanjalic, Alan
%B User Modeling, Adaption and Personalization
%C Berlin, Heidelberg
%D 2011
%E Konstan, Joseph A.
%E Conejo, Ricardo
%E Marzo, José L.
%E Oliver, Nuria
%I Springer Berlin / Heidelberg
%K cross-domain recommender shpaws tagging
%P 305--316
%R 10.1007/978-3-642-22362-4_26
%T Tags as Bridges between Domains: Improving Recommendation with Tag-Induced Cross-Domain Collaborative Filtering User Modeling, Adaption and Personalization
%U http://dx.doi.org/10.1007/978-3-642-22362-4_26
%V 6787
%X Recommender systems generally face the challenge of making predictions using only the relatively few user ratings available for a given domain. Cross-domain collaborative filtering (CF) aims to alleviate the effects of this data sparseness by transferring knowledge from other domains. We propose a novel algorithm, Tag-induced Cross-Domain Collaborative Filtering (TagCDCF), which exploits user-contributed tags that are common to multiple domains in order to establish the cross-domain links necessary for successful cross-domain CF. TagCDCF extends the state-of-the-art matrix factorization by introducing a constraint involving tag-based similarities between pairs of users and pairs of items across domains. The method requires no common users or items across domains. Using two publicly available CF data sets as different domains, we experimentally demonstrate that TagCDCF substantially outperforms other state-of-the-art single domain CF and cross-domain CF approaches. Additional experiments show that TagCDCF addresses data sparseness and illustrate the influence of the number of tags used by users in both domains.
%& 26
%@ 978-3-642-22361-7
@incollection{brusilovsky:Shi2011CrossDomain,
abstract = {{Recommender systems generally face the challenge of making predictions using only the relatively few user ratings available for a given domain. Cross-domain collaborative filtering (CF) aims to alleviate the effects of this data sparseness by transferring knowledge from other domains. We propose a novel algorithm, Tag-induced Cross-Domain Collaborative Filtering (TagCDCF), which exploits user-contributed tags that are common to multiple domains in order to establish the cross-domain links necessary for successful cross-domain CF. TagCDCF extends the state-of-the-art matrix factorization by introducing a constraint involving tag-based similarities between pairs of users and pairs of items across domains. The method requires no common users or items across domains. Using two publicly available CF data sets as different domains, we experimentally demonstrate that TagCDCF substantially outperforms other state-of-the-art single domain CF and cross-domain CF approaches. Additional experiments show that TagCDCF addresses data sparseness and illustrate the influence of the number of tags used by users in both domains.}},
added-at = {2018-03-19T12:24:51.000+0100},
address = {Berlin, Heidelberg},
author = {Shi, Yue and Larson, Martha and Hanjalic, Alan},
biburl = {https://www.bibsonomy.org/bibtex/28264156a7c405d35cc48444d5ac96606/aho},
booktitle = {User Modeling, Adaption and Personalization},
chapter = 26,
citeulike-article-id = {9541686},
citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-22362-4_26},
citeulike-linkout-1 = {http://www.springerlink.com/content/q47125u8q326j587},
doi = {10.1007/978-3-642-22362-4_26},
editor = {Konstan, Joseph A. and Conejo, Ricardo and Marzo, Jos\'{e} L. and Oliver, Nuria},
interhash = {377df252e7671f31a774505ee374f166},
intrahash = {8264156a7c405d35cc48444d5ac96606},
isbn = {978-3-642-22361-7},
keywords = {cross-domain recommender shpaws tagging},
pages = {305--316},
posted-at = {2012-04-08 18:14:06},
priority = {2},
publisher = {Springer Berlin / Heidelberg},
series = {Lecture Notes in Computer Science},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{Tags as Bridges between Domains: Improving Recommendation with Tag-Induced Cross-Domain Collaborative Filtering User Modeling, Adaption and Personalization}},
url = {http://dx.doi.org/10.1007/978-3-642-22362-4_26},
volume = 6787,
year = 2011
}