![]() There are several approaches that have been proposed in the literature to cater recommendations over such metrics, majority of which applies re-sorting the top-K items generated by a baseline recommendation algorithm. The qualities such as novelty and diversity are related with promotion of TLT items promoting TLT items have positive effect on novelty and sales diversity. In order to keep up the promises, RS has developed from accuracy centric DSS to other quality seeking DSS such as novelty and diversity. Recommending not so obvious yet useful recommendations adds value to the customers as well as impacts the return on investment (ROI) for e-commerce players. Since, popular items are anyway popular, recommending such popular and obvious recommendations do not add any significant value for the customers. It is in the interest of both e-commerce platforms and customers to deploy such RS which increases novelty and diversity of recommendation list. The key promise of a RS is to help the consumer discover new and relevant items outside their sphere of interest. Miss (non-hit) or insignificant number of hits for most of items are often referred as the long tail (TLT) phenomenon in the context of recommender systems. It has been observed that very few items account for many hits, some of the items are moderately hit and most of the items account for miss or non-hit. These niche products are mainly non-hit or miss products that account for significant sales in online platforms. Online retailers take the advantages of unlimited shelf space over brick and mortar retailers, with the aid of recommender systems (RS), by pushing the niche products to idiosyncratic users. Recommender systems are a specific type of personalized web-based decision support systems that analyse data about customers and products to help customers find items of interest. Recommender systems are designed to serve as important decision support systems for matching almost every customer’s expectations. The trend is to provide a unique shopping experience in the digital arena to every user so that overall customer satisfaction increases. Improving customer experience in the digital world is the prime focus of most e-commerce firms. Keywords: Collaborative filtering E-commerce Long-tail Matrix factorization Novelty Diversity 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. We also propose an adaptive model that pursues to promote the long tail items in the recommendation list. 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. ![]() In this paper, our focus is on matching the niche products to idiosyncratic users such that the needs of users are satiated. However, the current recommender systems, in general, recommends popular and obvious products leading to a few Long-Tail items. Owing to unlimited shelf space, it is in the interest of e-commerce platforms to push niche products to idiosyncratic users. The emergence of marketplace model provides platform for large fragmented buyers and sellers, where shelf space is not a constraint. It also helps e -commerce firms by pushing the range of products a user may purchase on their e-commerce platform. Recommender systems help consumers by reducing their search cost by directing them to interesting and useful products. Channelizing product sales with the aid of Recommender Systems is ubiquitous in e-commerce firms. ![]()
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