Empirical analysis of session-based recommendation algorithms

Malte Ludewig,Sara Latifi,Dietmar Jannach,Noemi Mauro

doi:10.1007/s11257-020-09277-1

Abstract

Recommender systems are tools that support online users by pointing them to potential items of interest in situations of information overload. In recent years, the class of session-based recommendation algorithms received more attention in the research literature. These algorithms base their recommendations solely on the observed interactions with the user in an ongoing session and do not require the existence of long-term preference profiles. Most recently, a number of deep learning-based (“neural”) approaches to session-based recommendations have been proposed. However, previous research indicates that today’s complex neural recommendation methods are not always better than comparably simple algorithms in terms of prediction accuracy. With this work, our goal is to shed light on the state of the art in the area of session-based recommendation and on the progress that is made with neural approaches. For this purpose, we compare twelve algorithmic approaches, among them six recent neural methods, under identical conditions on various datasets. We find that the progress in terms of prediction accuracy that is achieved with neural methods is still limited. In most cases, our experiments show that simple heuristic methods based on nearest-neighbors schemes are preferable over conceptually and computationally more complex methods. Observations from a user study furthermore indicate that recommendations based on heuristic methods were also well accepted by the study participants. To support future progress and reproducibility in this area, we publicly share the session-rec evaluation framework that was used in our research.

Highlights

Recommender systems (RS) are software applications that help users in situations of information overload, and they have become a common feature on many modern online services
Our work reveals that despite a continuous stream of papers that propose new neural approaches for session-based recommendation, the progress in the field seems still limited
Today’s deep learning techniques are in many cases not outperforming much simpler heuristic methods. This indicates that there still is a huge potential for more effective neural recommendation methods in the future in this area

Summary

Introduction

Recommender systems (RS) are software applications that help users in situations of information overload, and they have become a common feature on many modern online services. Collaborative filtering (CF) techniques, which are based on behavioral data collected from larger user communities, are among the most successful technical approaches in practice These approaches mostly rely on the assumption that information about longer-term preferences of the individual users is available, e.g., in the form of a user–item rating matrix (Resnick et al 1994). The difference is that instead of the long-term preference profiles only the observed interactions with the user in the ongoing session can be used to adapt the recommendations to the assumed needs, preferences, or intents of the user. Such a setting is usually termed a session-based recommendation problem (Quadrana et al 2018)

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Conclusion