Empirical analysis of session-based recommendation algorithms

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dc.contributor.authorLudewig, Malte
dc.contributor.authorMauro, Noemi
dc.contributor.authorLatifi, Sara
dc.contributor.authorJannach, Dietmar
dc.date.accessioned2021-06-21T10:47:16Z
dc.date.available2021-06-21T10:47:16Z
dc.date.issued2020-10-20
dc.description.abstractRecommender 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.en
dc.identifier.urihttp://hdl.handle.net/2003/40271
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22144
dc.language.isoende
dc.relation.ispartofseriesUser Model User-Adap Inter;31
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectSession-based recommendationen
dc.subjectPerformance evaluationen
dc.subjectReproducibilityen
dc.subject.ddc004
dc.subject.rswkEmpfehlungssystemde
dc.subject.rswkLeistungsbewertungde
dc.subject.rswkReproduzierbarkeitde
dc.titleEmpirical analysis of session-based recommendation algorithmsen
dc.title.alternativea comparison of neural and non-neural approachesen
dc.typeTextde
dc.type.publicationtypearticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationLudewig, M., Mauro, N., Latifi, S. et al. Empirical analysis of session-based recommendation algorithms. User Model User-Adap Inter 31, 149–181 (2021)de
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1007/s11257-020-09277-1de

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