Evaluation of session-based recommendation algorithms

Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term...

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Bibliographic Details
Published in:User modeling and user-adapted interaction Vol. 28; no. 4-5; pp. 331 - 390
Main Authors: Ludewig, Malte, Jannach, Dietmar
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.12.2018
Springer Nature B.V
Subjects:
Algorithms
Computer Science
Digital media
Management of Computing and Information Systems
Markov chains
Multimedia Information Systems
Neural networks
Recommender systems
Recurrent neural networks
User Interfaces and Human Computer Interaction
Sequential recommendation
Deep learning
Nearest neighbors
Session-based recommendation
Factorized Markov models
ISSN:0924-1868, 1573-1391
Online Access:Get full text
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Summary:Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session. Given the high practical relevance of the problem, an increased interest in this problem can be observed in recent years, leading to a number of proposals for session-based recommendation algorithms that typically aim to predict the user’s immediate next actions. In this work, we present the results of an in-depth performance comparison of a number of such algorithms, using a variety of datasets and evaluation measures. Our comparison includes the most recent approaches based on recurrent neural networks like gru4rec , factorized Markov model approaches such as fism or fossil , as well as simpler methods based, e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of this latter class, despite their sometimes almost trivial nature, often perform equally well or significantly better than today’s more complex approaches based on deep neural networks. Our results therefore suggest that there is substantial room for improvement regarding the development of more sophisticated session-based recommendation algorithms.
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ISSN:0924-1868
1573-1391
DOI:10.1007/s11257-018-9209-6