Improved session-based recommender system by context awareness in e-commerce domain

Over the past two decades, there has been a rapid increase in the number of online sales. This has resulted in an increase in the data collected about the users' behaviour which has aided the development of several novel Recommender System (RS) methods. One of the main problem in RS is the lack of "explicit rating"; many customers do not rate the items they buy or view. However, the user behaviour (e.g. session duration, number of items, item duration view, etc.) during an online session could give an indication about what the user preferences "implicit rating".
In this paper, we present a method to derive numerical implicit ratings from user browsing behaviour. Also, we examine the impact of using the derived numerical implicit ratings as context factors on some of the main RS methods, i.e. Factorisation Recommender and Item-Item Collaborative Filtering models. We evaluate the performance of the proposed framework on two large e-commerce datasets. The computational experiments show that in the absence of user explicit rating, the use of the user behaviour data to generate numerical implicit ratings could significantly improve the several RS methods.