Abstract
Recommender systems are applications to support users in searching relevant information or items on the Internet. Such examples are/news, articles, products in e-stores, books in libraries, music in broadcasting media. Recently, a new approach to recommendation generation emerged that concentrates on short-term users’ activities organized in sessions. Such an approach is effective in real-world solutions due to a predominant number of one-time users and a limited time of items’ availability. Session-based recommender systems are algorithms that focus particularly on users’ ongoing sessions with the aim to predict their next actions. This article presents results of experiments with session-based recommenders which rely on an active user’s neighbourhood identification. The algorithms were used in a standard form, where the neighbourhood was calculated with k-nearest neighbours and in a form of clusters generated in advance by clustering methods. The cluster-based approach was more efficient in terms of accuracy and diversity of recommendation lists.
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Acknowledgments
The work was supported by the grant from Bialystok University of Technology WZ/WI-IIT/2/2020 and funded with resources for research by the Ministry of Education and Science in Poland.
I would also like to thank Prof. Dietmar Jannach for a consultation and his support and Sara Latifi for evaluation framework advice.
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Kużelewska, U. (2022). Clustering Algorithms for Efficient Neighbourhood Identification in Session-Based Recommender Systems. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) New Advances in Dependability of Networks and Systems. DepCoS-RELCOMEX 2022. Lecture Notes in Networks and Systems, vol 484. Springer, Cham. https://doi.org/10.1007/978-3-031-06746-4_14
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