Authors: Ludewig, Malte
Mauro, Noemi
Latifi, Sara
Jannach, Dietmar
Title: Empirical analysis of session-based recommendation algorithms
Other Titles: a comparison of neural and non-neural approaches
Language (ISO): en
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.
Subject Headings: Session-based recommendation
Performance evaluation
Reproducibility
Subject Headings (RSWK): Empfehlungssystem
Leistungsbewertung
Reproduzierbarkeit
URI: http://hdl.handle.net/2003/40271
http://dx.doi.org/10.17877/DE290R-22144
Issue Date: 2020-10-20
Rights link: https://creativecommons.org/licenses/by/4.0/
Appears in Collections:LS 13 Dienstleistungsinformatik

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