Empirical analysis of session-based recommendation algorithms
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Empirical analysis of session‑based recommendation algorithms A comparison of neural and non-neural approaches Malte Ludewig1 · Noemi Mauro2 · Sara Latifi3 · Dietmar Jannach3 Received: 1 November 2019 / Accepted in revised form: 12 September 2020 © The Author(s) 2020
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 longterm 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 sessionbased 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. Keywords Session-based recommendation · Performance evaluation · Reproducibility This work combines and significantly extends our own previous work published in Ludewig and Jannach (2019) and Ludewig et al. (2019). This paper or a similar version is not currently under review by a journal or conference. This paper is void of plagiarism or self-plagiarism as defined by the Committee on Publication Ethics and Springer Guidelines. A preprint version of this work is available at https://arxiv.org/abs/1910.12781. Extended author information available on the last page of the article
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1 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. Historically, these approaches mostly rely on the assumption that information about longer-term preferences of the individual users is avai
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