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.

The industrial machine learning applications today involve developing and deploying MLOps pipelines to ensure the versatile quality of forecasting models over an extended period, simultaneously assuring the model's accuracy, stability, short training time, and resilience.In this study, we present the ML pipeline conforming to all the abovementioned aspects of models' quality formulated as a constrained multi-objective optimization problem.We also provide the reference implementation on stateof-the-art methods for data preprocessing, feature extraction, dimensionality reduction, feature and instance selection, model fitting, and ensemble blending.The experimental study on the real data set from the logistics industry confirmed the qualities of the proposed approach, as the successful participation in an international data competition did.