Fattening The Long Tail Items in E-Commerce

Abstract: 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 m...

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Autores principales:	Kumar,Bipul, Bala,Pradip Kumar
Lenguaje:	English
Publicado:	Universidad de Talca 2017
Materias:	Collaborative filtering E-commerce Long-tail Matrix factorization Novelty Diversity
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762017000300004
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spelling	oai:scielo:S0718-187620170003000042018-01-11Fattening The Long Tail Items in E-CommerceKumar,BipulBala,Pradip Kumar Collaborative filtering E-commerce Long-tail Matrix factorization Novelty Diversity Abstract: 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.info:eu-repo/semantics/openAccessUniversidad de TalcaJournal of theoretical and applied electronic commerce research v.12 n.3 20172017-09-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762017000300004en10.4067/S0718-18762017000300004
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Collaborative filtering E-commerce Long-tail Matrix factorization Novelty Diversity
spellingShingle	Collaborative filtering E-commerce Long-tail Matrix factorization Novelty Diversity Kumar,Bipul Bala,Pradip Kumar Fattening The Long Tail Items in E-Commerce
description	Abstract: 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.
author	Kumar,Bipul Bala,Pradip Kumar
author_facet	Kumar,Bipul Bala,Pradip Kumar
author_sort	Kumar,Bipul
title	Fattening The Long Tail Items in E-Commerce
title_short	Fattening The Long Tail Items in E-Commerce
title_full	Fattening The Long Tail Items in E-Commerce
title_fullStr	Fattening The Long Tail Items in E-Commerce
title_full_unstemmed	Fattening The Long Tail Items in E-Commerce
title_sort	fattening the long tail items in e-commerce
publisher	Universidad de Talca
publishDate	2017
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762017000300004
work_keys_str_mv	AT kumarbipul fatteningthelongtailitemsinecommerce AT balapradipkumar fatteningthelongtailitemsinecommerce
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Fattening The Long Tail Items in E-Commerce

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