| Autor: |
Kannout, Eyad, Grodzki, Michał, Grzegorowski, Marek |
| Zdroj: |
Computer Science & Information Systems; Sep2023, Vol. 20 Issue 4, p1343-1366, 24p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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