An integrated recommender system for improved accuracy and aggregate diversity

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dc.contributor.authorBag, Sujoy
dc.contributor.authorGhadge, Abhijeet
dc.contributor.authorTiwari, Manoj Kumar
dc.date.accessioned2019-02-25T18:54:13Z
dc.date.available2019-02-25T18:54:13Z
dc.date.issued2019-02-19
dc.description.abstractInformation explosion creates dilemma in finding preferred products from the digital marketplaces. Thus, it is challenging for online companies to develop an efficient recommender system for large portfolio of products. The aim of this research is to develop an integrated recommender system model for online companies, with the ability of providing personalized services to their customers. The K-nearest neighbors (KNN) algorithm uses similarity matrices for performing the recommendation system; however, multiple drawbacks associated with the conventional KNN algorithm have been identified. Thus, an algorithm considering weight metric is used to select only significant nearest neighbors (SNN). Using secondary dataset on MovieLens and combining four types of prediction models, the study develops an integrated recommender system model to identify SNN and predict accurate personalized recommendations at lower computation cost. A timestamp used in the integrated model improves the performance of the personalized recommender system. The research contributes to behavioral analytics and recommender system literature by providing an integrated decision-making model for improved accuracy and aggregate diversity. The proposed prediction model helps to improve the profitability of online companies by selling diverse and preferred portfolio of products to their customers.
dc.identifier.citationBag S, Ghadge A, Tiwari MK. (2019) An integrated recommender system for improved accuracy and aggregate diversity. Computers and Industrial Engineering. Volume 130, April 2019, pp. 187-197
dc.identifier.issn0360-8352
dc.identifier.urihttp://doi.org/10.1016/j.cie.2019.02.028
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/13939
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectRecommender systemen_UK
dc.subjectBehavioral analyticsen_UK
dc.subjectExtreme learningen_UK
dc.subjectAggregate diversityen_UK
dc.subjectE-businessen_UK
dc.subjectDecision support systemen_UK
dc.titleAn integrated recommender system for improved accuracy and aggregate diversityen_UK
dc.typeArticleen_UK

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