File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: A Revisit to Social Network-Based Recommender Systems

TitleA Revisit to Social Network-Based Recommender Systems
Authors
KeywordsRecommender System
Social Network
Social Influence
Issue Date2014
PublisherAssociation for Computing Machinery (ACM).
Citation
Proceedings of the 37th Annual International Association for Computing Machinery (ACM) Special Interest Group On Information Retrieval (SIGIR) Conference, Gold Coast, Australia, 6-11 July 2014, p. 1239-1242 How to Cite?
AbstractWith the rapid expansion of online social networks, social network-based recommendation has become a meaningful and effective way of suggesting new items or activities to users. In this paper, we propose two methods to improve the performance of the state-of-art social network-based recommender system (SNRS), which is based on a probabilistic model. Our first method classifies the correlations between pairs of users' ratings. The other is making the system robust to sparse data, i.e., few immediate friends having few common ratings with the target user. Our experimental study demonstrates that our techniques significantly improve the accuracy of SNRS.
Persistent Identifierhttp://hdl.handle.net/10722/198601
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLi, Hen_US
dc.contributor.authorWu, Den_US
dc.contributor.authorMamoulis, Nen_US
dc.date.accessioned2014-07-07T08:09:39Z-
dc.date.available2014-07-07T08:09:39Z-
dc.date.issued2014en_US
dc.identifier.citationProceedings of the 37th Annual International Association for Computing Machinery (ACM) Special Interest Group On Information Retrieval (SIGIR) Conference, Gold Coast, Australia, 6-11 July 2014, p. 1239-1242en_US
dc.identifier.isbn9781450322577-
dc.identifier.urihttp://hdl.handle.net/10722/198601-
dc.description.abstractWith the rapid expansion of online social networks, social network-based recommendation has become a meaningful and effective way of suggesting new items or activities to users. In this paper, we propose two methods to improve the performance of the state-of-art social network-based recommender system (SNRS), which is based on a probabilistic model. Our first method classifies the correlations between pairs of users' ratings. The other is making the system robust to sparse data, i.e., few immediate friends having few common ratings with the target user. Our experimental study demonstrates that our techniques significantly improve the accuracy of SNRS.-
dc.languageengen_US
dc.publisherAssociation for Computing Machinery (ACM).-
dc.relation.ispartofProceedings of the Annual ACM SIGIR Conferenceen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectRecommender System-
dc.subjectSocial Network-
dc.subjectSocial Influence-
dc.titleA Revisit to Social Network-Based Recommender Systemsen_US
dc.typeConference_Paperen_US
dc.identifier.emailWu, D: dmwu@cs.hku.hken_US
dc.identifier.emailMamoulis, N: nikos@cs.hku.hken_US
dc.identifier.authorityMamoulis, N=rp00155en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1145/2600428.2609554-
dc.identifier.hkuros230025en_US
dc.identifier.spage1239-
dc.identifier.epage1242-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats