Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3444
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dc.contributor.authorAgarwal, Pragati-
dc.contributor.authorSreepada, RamaSyamala-
dc.contributor.authorPatra, Bidyut Kr.-
dc.date.accessioned2020-01-03T06:11:45Z-
dc.date.available2020-01-03T06:11:45Z-
dc.date.issued2019-12-
dc.identifier.citation8th International Conference onPattern Recognition and Machine Intelligence (PReMI 2019), Tezpur University, Assam, 17-20 December , 2019en_US
dc.identifier.urihttp://hdl.handle.net/2080/3444-
dc.descriptionThe copy right of this document belongs to the Proceedings publisher.en_US
dc.description.abstractIn today’s information overloaded era, recommender system is a necessity and it is widely used in most of the domains of e-commerce.Over the years, recommender system is improved to meet the main purpose of achieving better user experience, where accuracy is considered as one of the important aspects in its design. However, other aspects such as diversity, long tail item recommendation, novelty and serendipity are equally important while providing recommendations to the users. Re-search to improve above mentioned aspects is limited. In this paper, we propose an efficient approach to improve diversity and long tail item recommendations. The experiments are conducted on two real world movie rating data sets namely, Movie Lens and Netflix. Experimental analysis shows that the proposed method outperforms the state-of-the art approaches in recommending diverse and long tail itemsen_US
dc.subjectRecommender Systemen_US
dc.subjectDiversityen_US
dc.subjectCollaborative Filteringen_US
dc.subjectLong Tail Itemsen_US
dc.subjectHybrid Reranking Frameworken_US
dc.titleA HybridFramework for ImprovingDiversityandLongTailItemsinRecommendationsen_US
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