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dc.contributor.authorHimabindu, Tadiparthi VR
dc.contributor.authorPadmanabhan, Vineet
dc.contributor.authorPujari, Arun K
dc.contributor.authorSattar, Abdul
dc.contributor.editorBooth, R
dc.contributor.editorZhang, ML
dc.date.accessioned2017-12-13T03:38:19Z
dc.date.available2017-12-13T03:38:19Z
dc.date.issued2016
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-319-42911-3_11
dc.identifier.urihttp://hdl.handle.net/10072/123831
dc.description.abstractRecommender systems can be viewed as prediction systems where we can predict the ratings which represent users’ interest in the corresponding item. Typically, items having the highest predicted ratings will be recommended to the users. But users do not know how certain these predictions are. Therefore, it is important to associate a confidence measure to the predictions which tells users how certain the system is in making the predictions. Many different approaches have been proposed to estimate confidence of predictions made by recommender systems. But none of them provide guarantee on the error rate of these predictions. Conformal Prediction is a framework that produces predictions with a guaranteed error rate. In this paper, we propose a conformal prediction algorithm with item-based collaborative filtering as the underlying algorithm which is a simple and widely used algorithm in commercial applications. We propose different nonconformity measures and empirically determine the best nonconformity measure. We empirically prove validity and efficiency of proposed algorithm. Experimental results demonstrate that the predictive performance of conformal prediction algorithm is very close to its underlying algorithm with little uncertainty along with the measures of confidence and credibility.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofpagefrom125
dc.relation.ispartofpageto138
dc.relation.ispartofjournalLecture Notes in Computer Science
dc.relation.ispartofvolume9810
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classified
dc.subject.fieldofresearchcode089999
dc.titlePrediction with Confidence in Item Based Collaborative Filtering
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorSattar, Abdul


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