Promoting Tail Item Recommendations in E-Commerce

The research area of recommender systems (RS) in e-commerce has become extremely popular in recent years. However, traditional RSs tend to recommend popular items, while niche (long-tail) items are often neglected, which is known as the long-tail problem. However, recent studies found that tail items are one of the key success factors in the e-commerce world. The availability of such items encompasses relatively high marginal profits and boosts the sales of popular short-head items. We suggest promoting long-tail items by leveraging the short-head items' advantages and exposing the user to a tail item that may have not been considered otherwise. We use a classification model and statistical tools to generate personalized recommendations of a long-tail item considering a short-head item that has already been clicked. The uniqueness of our method lies in the combination of tail and head items to uplift the exposure of the latter and in using an applicable solution to deal with the extreme volume of tail items. We demonstrate the effectiveness of our method on real-world data from eBay and provide an analysis of the long-tail phenomenon and consumption behavior.