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Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems

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DOI: http://dx.doi.org/10.15439/2022F86

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 217–226 ()

Full text

Abstract. Although several approaches have been proposed throughout the last decade to build recommender systems (RS), most of them suffer from the cold-start problem. This problem occurs when a new item hits the system or a new user signs up. It is generally recognized that the ability to handle cold users and items is one of the key success factors of any new recommender algorithm. This paper introduces a frequent pattern mining framework for recommender systems (FPRS) - a novel approach to address this challenging task. FPRS is a hybrid RS that incorporates collaborative and content-based recommendation algorithms and employs a frequent pattern (FP) growth algorithm. The article proposes several strategies to combine the generated frequent itemsets with content-based methods to mitigate the cold-start problem for both new users and new items. The performed empirical evaluation confirmed its usefulness. Furthermore, the developed solution can be easily combined with any other approach to build a recommender system and can be further extended to make up a complete and standalone RS.

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