Context Clustering-based Recommender Systems. - Eyad Kannout (FedCSIS, 2020)

Toward Recommender Systems Scalability and Efficacy. - Eyad Kannout; Marek Grzegorowski; Hung Son Nguyen (CS&P (Revised Selected Papers), 2021)

Speeding Up Recommender Systems Using Association Rules. - Eyad Kannout; Hung Son Nguyen; Marek Grzegorowski (ACIIDS (2), 2022)

Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems. - Eyad Kannout; Michal Grodzki; Marek Grzegorowski (FedCSIS, 2022)

Considering various aspects of models' quality in the ML pipeline - application in the logistics sector. - Eyad Kannout; Michal Grodzki; Marek Grzegorowski (FedCSIS, 2022)

Towards addressing item cold-start problem in collaborative filtering by embedding agglomerative clustering and FP-growth into the recommendation system. - Eyad Kannout; Michal Grodzki; Marek Grzegorowski (Comput. Sci. Inf. Syst., 2023)

Forecasting Stock Trends with Feedforward Neural Networks. - Marcin Traskowski; Eyad Kannout (FedCSIS, 2024)

Clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems. - Eyad Kannout; Marek Grzegorowski; Michal Grodzki; Hung Son Nguyen (IEEE Access, 2024)