Collaborative Filtering: The Aim of Recommender Systems and the Significance of User Ratings

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dc.cclicenceCC-BY-NCen
dc.contributor.authorRedpath, J.en
dc.contributor.authorGlass, D.en
dc.contributor.authorChen, Limingen
dc.contributor.authorMcClean, S.I.en
dc.date.acceptance2010-01-19en
dc.date.accessioned2018-03-08T11:53:46Z
dc.date.available2018-03-08T11:53:46Z
dc.date.issued2010-03
dc.description.abstractThis paper investigates the significance of numeric user ratings in recommender systems by considering their inclusion / exclusion in both the generation and evaluation of recommendations. When standard evaluation metrics are used, experimental results show that inclusion of numeric rating values in the recommendation process does not enhance the results. However, evaluating the accuracy of a recommender algorithm requires identifying the aim of the system. Evaluation metrics such as precision and recall evaluate how well a system performs at recommending items that have been previously rated by the user. By contrast, a new metric, known as Approval Rate, is intended to evaluate how well a system performs at recommending items that would be rated highly by the user. Experimental results demonstrate that these two aims are not synonymous and that for an algorithm to attempt both obscures the investigation. The results also show that appropriate use of numeric rating valuesin the process of calculating user similarity can enhance the performance when Approval Rate is used.en
dc.funderN/Aen
dc.identifier.citationRedpath, J., Glass, D.H., McClean, S., Chen, L. (2010) Collaborative Filtering: The Aim of Recommender Systems and the Significance of User Ratings. In: Gurrin, C. et al. (eds) Advances in Information Retrieval. ECIR 2010. Lecture Notes in Computer Science, 5993, Berlin: Springer. pp.394-406en
dc.identifier.doihttps://doi.org/10.1007/978-3-642-12275-0_35
dc.identifier.isbn9783642122743
dc.identifier.urihttp://hdl.handle.net/2086/15397
dc.language.isoenen
dc.peerreviewedYesen
dc.projectidN/Aen
dc.publisherSpringeren
dc.researchgroupCIIRGen
dc.researchinstituteCyber Technology Institute (CTI)en
dc.subjectRecommender Systemsen
dc.subjectCollaborative Filteringen
dc.subjectInformation Retrievalen
dc.titleCollaborative Filtering: The Aim of Recommender Systems and the Significance of User Ratingsen
dc.typeBook chapteren

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