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 may purchase on their ecommerce 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.