Hybrid session-aware recommendation with feature-based models

Abstract

Session-based recommender systems model the interests of users based on their browsing behavior with the goal of making suitable item suggestions in an ongoing usage session. Most existing work in this growing research area make only use of the most recent observed interactions for each user, and they typically solely rely on user–item interaction data (e.g., click events) for interest modeling. Thus, they do not leverage important forms of other information which are commonly available in practical settings. In this work, we therefore propose a hybrid approach for personalized session-based (“session-aware”) recommendation, which (i) is able to take into account various types of side information as model features and which (ii) can be combined with existing session-based (or session-aware) recommendation models. Technically, our approach is based on stacking several session-based modeling approaches with efficient machine learning methods for tabular data, in our case using Gradient Boosting Machines (GBMs). We successfully evaluated our approach (named HySAR) on two public e-commerce datasets. Specifically, we also demonstrate the effectiveness of a number of novel model features that we engineered in the course of this research. These features, which were mostly unexplored in previous works, relate to various types of information related to the users, their actions, the items, as well as contextual session characteristics. Different existing recommendation approaches and further problem specific features can be easily added in our generic method to improve recommendations.