Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems
Eyad Kannout, Michał Grodzki, Marek Grzegorowski · Annals of Computer Science and Information Systems · 2022
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.Index Terms-recommendation system, cold-start problem, frequent pattern mining, quality of recommendations.