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dc.creatorKumar,Bipul
dc.creatorBala,Pradip Kumar
dc.date2017-09-01
dc.date.accessioned2019-04-25T12:42:02Z
dc.date.available2019-04-25T12:42:02Z
dc.identifierhttps://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0718-18762017000300004
dc.identifier.urihttp://revistaschilenas.uchile.cl/handle/2250/61297
dc.descriptionAbstract: Channelizing product sales with the aid of Recommender Systems is ubiquitous in e-commerce firms. Recommender systems help consumers by reducing their search cost by directing them to interesting and useful products. It also helps e -commerce firms by pushing the range of products a user may purchase on their e-commerce platform. The emergence of marketplace model provides platform for large fragmented buyers and sellers, where shelf space is not a constraint. Owing to unlimited shelf space, it is in the interest of e-commerce platforms to push niche products to idiosyncratic users. However, the current recommender systems, in general, recommends popular and obvious products leading to a few Long-Tail items. In this paper, our focus is on matching the niche products to idiosyncratic users such that the needs of users are satiated. We propose an innovative and robust model of matrix factorization that engenders recommendations based on a user’s optimal liking of the long-tail items. We also propose an adaptive model that pursues to promote the long tail items in the recommendation list. Comprehensive empirical evaluations consistently show the gains of the proposed techniques for handling the long tail on real world data sets like Amazon dataset over different algorithms.
dc.formattext/html
dc.languageen
dc.publisherUniversidad de Talca
dc.relation10.4067/S0718-18762017000300004
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceJournal of theoretical and applied electronic commerce research v.12 n.3 2017
dc.subjectCollaborative filtering
dc.subjectE-commerce
dc.subjectLong-tail
dc.subjectMatrix factorization
dc.subjectNovelty
dc.subjectDiversity
dc.titleFattening The Long Tail Items in E-Commerce


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