Abstract: Fuzzy clustering measures the ambiguity of user reviews and groups similar users into the same cluster, which can improve the scalability and address data sparsity issues in traditional collaborative filtering algorithms. However, existing collaborative filtering algorithms based on fuzzy clustering often overlook the problems of cluster center initialization and fuzzy set weighting, leading to unstable clustering results and an inability to fully utilize review information, which in turn affects recommendation accuracy. To address these issues, this paper proposes a collaborative filtering recommendation algorithm fusing ROUSTIDA and improved probabilistic intuitionistic fuzzy clustering. The algorithm fills in missing data based on attribute reduction rules from rough set theory and the principle of minimiz- ing the difference between the missing matrix and the similarity matrix, thereby reducing data sparsity. It introduces a density function-based initialization method for selecting cluster centers, mitigating the high sensitivity of fuzzy clustering to initial cluster centers. During clustering computation, it separately calculates the probability weights of membership and non-membership degrees, as well as the correlation coefficients of hesitation degrees, using a weighted probabilistic Euclidean distance as the proximity function for clustering to filter out relevant neighbor sets. This approach retains more user review information during the clustering process. Experimental results on MovieLens 100K and Jester datasets demonstrate that, compared to other fuzzy clustering-based recommendation algorithms such as UFCM and FCM-Slope One, the proposed algorithm achieves lower mean absolute error (MAE) and root mean square error (RMSE) values, indicating superior recommendation accuracy.

Key words: collaborative filtering, ROUSTIDA algorithm, density function, improved probabilistic intuitionistic fuzzy clustering, recommendation algorithm