dc.contributor.authorGuo, Guibing
dc.contributor.authorZhang, Jie
dc.contributor.authorYorke-Smith, Neil
dc.date.accessioned2017-01-06T05:15:38Z
dc.date.available2017-01-06T05:15:38Z
dc.date.issued2016
dc.identifier.citationGuo, G., Zhang, J., & Yorke-Smith, N. (2016). A Novel Evidence-Based Bayesian Similarity Measure for Recommender Systems. ACM Transactions on the Web, 10(2), 8-.en_US
dc.identifier.issn1559-1131en_US
dc.identifier.urihttp://hdl.handle.net/10220/41981
dc.description.abstractUser-based collaborative filtering, a widely used nearest neighbour-based recommendation technique, predicts an item’s rating by aggregating its ratings from similar users. User similarity is traditionally calculated by cosine similarity or the Pearson correlation coefficient. However, both of these measures consider only the direction of rating vectors, and suffer from a range of drawbacks. To overcome these issues, we propose a novel Bayesian similarity measure based on the Dirichlet distribution, taking into consideration both the direction and length of rating vectors. We posit that not all the rating pairs should be equally counted in order to accurately model user correlation. Three different evidence factors are designed to compute the weights of rating pairs. Further, our principled method reduces correlation due to chance and potential system bias. Experimental results on six real-world datasets show that our method achieves superior accuracy in comparison with counterparts.en_US
dc.description.sponsorshipMOE (Min. of Education, S’pore)en_US
dc.format.extent30 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesACM Transactions on the Weben_US
dc.rights© 2016 Association for Computing Machinery (ACM). This is the author created version of a work that has been peer reviewed and accepted for publication by ACM Transactions on the Web, Association for Computing Machinery (ACM). It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1145/2856037].en_US
dc.subjectRecommender systemsen_US
dc.subjectBayesian similarityen_US
dc.titleA Novel Evidence-Based Bayesian Similarity Measure for Recommender Systemsen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1145/2856037
dc.description.versionAccepted versionen_US


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