Journal's Facebook pageTowards Addressing Item Cold-Start Problem in Collaborative Filtering by Embedding Agglomerative Clustering and FP-Growth into the Recommendation System
- Institute of Informatics, University of Warsaw
Banacha 2, Warsaw, Poland
{eyad.kannout,m.grzegorowski}@mimuw.edu.pl, m.grodzki@students.mimuw.edu.pl
Abstract
This paper introduces a frequent pattern mining framework for recommender systems (FPRS) - a novel approach to address the items’ cold-start problem. This difficulty occurs when a new item hits the system, and properly handling such a situation is one of the key success factors of any deployment. The article proposes several strategies to combine collaborative and content-based filtering methods with frequent items mining and agglomerative clustering techniques to mitigate the cold-start problem in recommender systems. The experiments evaluated the developed methods against several quality metrics on three benchmark datasets. The conducted study confirmed usefulness of FPRS in providing apt outcomes even for cold items. The presented solution can be integrated with many different approaches and further extended to make up a complete and standalone RS.
Key words
recommendation system, cold-start problem, frequent pattern mining, quality of recommendations
Digital Object Identifier (DOI)
https://doi.org/10.2298/CSIS221116052K
Publication information
Volume 20, Issue 4 (September 2023)
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
Full text
Available in PDF
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How to cite
Kannout, E., Grodzki, M., Grzegorowski, M.: Towards Addressing Item Cold-Start Problem in Collaborative Filtering by Embedding Agglomerative Clustering and FP-Growth into the Recommendation System. Computer Science and Information Systems, Vol. 20, No. 4. (2023), https://doi.org/10.2298/CSIS221116052K
