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dc.contributor.authorPosoldova, Alexandra
dc.contributor.authorLiew, Alan Wee-Chung
dc.date.accessioned2018-07-02T01:30:27Z
dc.date.available2018-07-02T01:30:27Z
dc.date.issued2015
dc.identifier.isbn9781467390965
dc.identifier.doi10.1109/INISTA.2015.7276720
dc.identifier.urihttp://hdl.handle.net/10072/340865
dc.description.abstracthe amount of available content of different types of services is so large nowadays that one cannot realistically have a real time overview of the content. Recommendation engines were developed to solve the problem of information overload, and save time and effort when looking for appealing content. In this paper, we present an enhanced Naïve Bayes model for rating prediction of a program based on content description information. As our prediction model has to deal with categorical data, a probabilistic Bayesian network is used. The model uses a set of features to predict user rating based on past observation. We also simulated recommendation from a program offer. The recommendation system presented in this paper is flexible and robust enough to handle a sparse data set with very few records of feature description. Experiments were performed on a Yahoo movie data set and they indicated the promising performance of our approach over an existing technique.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States of America
dc.relation.ispartofconferencenameInternational Symposium on Innovations in Intelligent SysTems and Applications (INISTA 2015)
dc.relation.ispartofconferencetitle2015 INTERNATIONAL SYMPOSIUM ON INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS (INISTA) PROCEEDINGS
dc.relation.ispartofdatefrom2015-09-02
dc.relation.ispartofdateto2015-09-04
dc.relation.ispartoflocationMadrid, SPAIN
dc.relation.ispartofpagefrom12
dc.relation.ispartofpagefrom8 pages
dc.relation.ispartofpageto19
dc.relation.ispartofpageto8 pages
dc.relation.ispartofedition1st
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchcode460299
dc.titleContent based recommendation for HBB TV based on bayes conditional probability for multiple variables approach
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorLiew, Alan Wee-Chung


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    Contains papers delivered by Griffith authors at national and international conferences.

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