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Our approach can leverage the collective preferences of users within each cluster to generate more accurate recommendations, even when individual user data are limited, to alleviate the data sparsity problem. By limiting similarity assessments to user clusters, our approach improves the scalability of the recommendation algorithm. To address the dynamic nature of user interests, we propose a time decay function that extends the base-level learning function, assigning higher weights to recent ratings for a more accurate representation of current user preferences. The performance of the recommendation algorithm is considerably influenced by the accuracy of the clustering process. Traditional fuzzy clustering methods often prioritize distance during iterations, potentially disregarding the overall consistency of data points, while random initialization of cluster centers can reduce stability and increase susceptibility to noise. We tackle these issues by employing a density-sensitive distance measure for proximity calculations and integrating fuzzy entropy into the clustering process, ensuring global consistency and improving the handling of complex data structures. Moreover, we introduce a mixed-distance approach for selecting initial cluster centers, enhancing the robustness of the clustering algorithm. Experimental results across multiple datasets validate the effectiveness of our proposed method in improving recommendation accuracy and user satisfaction.<\/jats:p>","DOI":"10.34133\/icomputing.0188","type":"journal-article","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T06:39:21Z","timestamp":1756103961000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":1,"title":["Improved Collaborative Filtering for Recommendations: Integrating Fuzzy Clustering and Temporal Dynamics Analysis"],"prefix":"10.34133","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7567-2850","authenticated-orcid":false,"given":"Senlin","family":"Mao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, \rNanjing University of Aeronautics and Astronautics, 210016 Nanjing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, \rNanjing University of Aeronautics and Astronautics, 210016 Nanjing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianzi","family":"Zang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, \rNanjing University of Aeronautics and Astronautics, 210016 Nanjing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao","family":"Li","sequence":"additional","affiliation":[{"name":"Hangzhou Yugu Technology Co, Ltd.","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, \rNanjing University of Aeronautics and Astronautics, 210016 Nanjing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"Iqbal M. 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