Implicit Feedback Awareness for Session Based Recommendation in E-Commerce

Abstract

Information overload is a challenge in e-commerce platforms. E-shoppers may have difficulty selecting the best product from the available options. Recommender systems (RS) can filter relevant products according to user’s preferences, interest or observed user behaviours while they browse products on e-commerce platforms. However, collecting users’ explicit preferences for the products on these platforms is a difficult process since buyers prefer to rate the products after they use them rather than while they are looking for products. Therefore, to generate next product recommendations in the e-commerce domain, mostly shoppers’ click behaviour is taken into consideration. Shoppers could indicate their interest in the products in different ways. Spending more time on a product could imply a different level of user interest than skipping quickly the product or adding basket behaviour could show more intense interest than just browsing. In this study, we investigate the effect of applying the generated explicit ratings on RS by implementing a framework that maps users’ implicit feedback into explicit ratings in the e-commerce domain. We conduct computational experiments on well-known RS algorithms using two datasets containing mapped explicit ratings. The results of the experimental analysis indicate that incorporating calculated explicit ratings from users’ implicit feedback can help RS models perform better. The results suggest that there is more performance gap between using implicit and explicit ratings when factorisation machine RS model is used.