Book recommendation based on joint multi-relational model

Shangguan, Q; Hu, L; Cao, J; Xu, G

Book recommendation based on joint multi-relational model

Shangguan, Q Hu, L

Cao, J Xu, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications, CGC/SCA 2012, 2012, pp. 523 - 530
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Shangguan, Q	en_US
dc.contributor.author	Hu, L https://orcid.org/0000-0001-8588-2177	en_US
dc.contributor.author	Cao, J	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Proceedings - 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications, CGC/SCA 2012, 2012, pp. 523 - 530	en_US
dc.identifier.isbn	9780769548647	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32938
dc.description.abstract	Recommender system, which is powerful to deal with the issue of information overload, has been widely investigated by many researchers recently. However, one of the biggest challenges needs to face is the cold start problem. To address this problem, the data source from social network is incorporated into our recommender system in this paper. In a social network, users who tightly connected imply some group-specific interests. Consequently, we may exploit social network information to resolve the cold start problem and improve prediction performance. The main motivation of this paper is to exploit social relationships and other extra data sources to adjust the latent factors learning over the target matrix, namely book rating matrix and a group of auxiliary matrices, typically, the social relationship matrix. Our recommender system is based on coupled matrix factorization in major, and utilizes the random walk and genetic algorithm to learn some special parameters. The data for experiments is crawled from one of the Chinese biggest reading-sharing website, Douban. Finally, the results have proved that our book recommender system incorporating auxiliary data sources has much better performance than traditional methods. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications, CGC/SCA 2012	en_US
dc.relation.isbasedon	10.1109/CGC.2012.53	en_US
dc.title	Book recommendation based on joint multi-relational model	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Xiangtan, China
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Recommender system, which is powerful to deal with the issue of information overload, has been widely investigated by many researchers recently. However, one of the biggest challenges needs to face is the cold start problem. To address this problem, the data source from social network is incorporated into our recommender system in this paper. In a social network, users who tightly connected imply some group-specific interests. Consequently, we may exploit social network information to resolve the cold start problem and improve prediction performance. The main motivation of this paper is to exploit social relationships and other extra data sources to adjust the latent factors learning over the target matrix, namely book rating matrix and a group of auxiliary matrices, typically, the social relationship matrix. Our recommender system is based on coupled matrix factorization in major, and utilizes the random walk and genetic algorithm to learn some special parameters. The data for experiments is crawled from one of the Chinese biggest reading-sharing website, Douban. Finally, the results have proved that our book recommender system incorporating auxiliary data sources has much better performance than traditional methods. © 2012 IEEE.

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http://hdl.handle.net/10453/32938