Horasan, FahrettinYurttakal, Ahmet HaşimGündüz, Selçuk2025-01-212025-01-2120231319-15782213-1248https://doi.org/10.1016/j.jksuci.2023.101724https://hdl.handle.net/20.500.12587/24098Collaborative filtering is a technique that takes into account the common characteristics of users and items in recommender systems. Matrix decompositions are one of the most used techniques in collabo-rative filtering based recommendation systems. Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF) based approaches are widely used. Although they are quite good at dealing with the scalability problem, their complexities are high. In this study, the Truncated-ULV decomposition (T-ULVD) technique was used as an alternative technique to improve the accuracy and quality of recom-mendations. The proposed method has been tested with Movielens 100 k, Movielens 1 M, Filmtrust, and Netflix datasets, which are widely used in recommender system researches. In order to assess the perfor-mance of the proposed model, standart metrics (MAE, RMSE, precision, recall, and F1 score) were used. It is seen that while progress was achieved in all experiments with the T-ULVD compared to the NMF, very close or better results were obtained compared to the SVD. Moreover, this study may guide T-ULVD based future studies on solving the cold-start problem and reducing the sparsity in collaborative filtering based recommender systems.& COPY; 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).eninfo:eu-repo/semantics/openAccessRecommender systems; Collaborative filtering; Information filtering; Truncated-ULV decomposition; Social recommendationArticle35810.1016/j.jksuci.2023.1017242-s2.0-85170055547Q1WOS:001073465900001Q1