Session-aware recommendation: A surprising quest for the state-of-the-art

Sara Latifi,Noemi Mauro,Dietmar Jannach

doi:10.1016/j.ins.2021.05.048

Sara Latifi, Noemi Mauro + Show 1 more

Open Access

https://doi.org/10.1016/j.ins.2021.05.048

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Abstract

Recommender systems are designed to help users in situations of information overload. In recent years we observed increased interest in session-based recommendation scenarios, where the problem is to make item suggestions to users based only on interactions observed in an ongoing session, e.g., on an e-commerce site. However, in cases where interactions from previous user sessions are also available, the recommendations can be personalized according to the users’ long-term preferences, a process called session-aware recommendation. Today, research in this area is scattered, and many works only compare a newly proposed session-aware with existing session-based models. This makes it challenging to understand what represents the state-of-the-art. To close this research gap, we benchmarked recent session-aware algorithms against each other and against a number of session-based recommendation algorithms along with heuristic extensions thereof. Our comparison, to some surprise, revealed that (i) simple techniques based on nearest neighbors consistently outperform recent neural techniques and that (ii) session-aware models were mostly not better than approaches that do not use long-term preference information. Our work therefore points to potential methodological issues where new methods are compared to weak baselines, and it also indicates that there remains a huge potential for more sophisticated session-aware recommendation algorithms.

Highlights

Recommender systems (RS) can nowadays be found on many modern e-commerce and media streaming sites, where they help users find items of interest in situations of information overload
The work presented in [16] was written in MATLAB but is only marginally relevant for our work, as it (i) focuses on diversity aspects and (ii) provides a performance comparison only with session-based or sequential approaches, whereas our work focuses on session-aware techniques
Our in-depth empirical investigation of five recent neural approaches to session-aware recommendation has revealed that these methods, contrary to the claims in the respective papers, are not effective at leveraging long-term preference information for improved recommendations

Summary

Introduction

Recommender systems (RS) can nowadays be found on many modern e-commerce and media streaming sites, where they help users find items of interest in situations of information overload. Researchers have strongly focused on situations where only information about long-term user preferences is available, e.g., in the form of item ratings. Due to the practical relevance of this problem, a variety of technical approaches to session-based recommendation were proposed in the past few years, in particular ones based on deep learning (neural) techniques, see [28,47]. These methods try to make recommendations by guessing the user’s short-term intent or situational context only from the

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