融合ROUSTIDA 和改进的 概率直觉模糊聚类的协同过滤推荐算法.
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| Title: | 融合ROUSTIDA 和改进的 概率直觉模糊聚类的协同过滤推荐算法. |
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| Alternate Title: | A collaborative filtering recommendation algorithm fusing ROUSTIDA and improved probabilistic intuitionistic fuzzy clustering. |
| Authors: | 张艳菊1,2 zhangyanju@lntu.edu.cn, 吴一玄1 18525436095@163.com, 陈泽荣1 |
| Source: | Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Apr2026, Vol. 48 Issue 4, p731-742. 12p. |
| Subjects: | Recommender systems, Fuzzy clustering technique, Consumers' reviews, Centroid, Missing data (Statistics), Rough sets |
| Abstract (English): | 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 minimizing 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 separatelycalculates 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. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): | 模糊聚类衡量用户评价的模糊性并将相似用户划分为同一簇,能够改善传统协同过滤算法的 可扩展性和数据稀疏性,但现有基于模糊聚类的协同过滤算法通常没有考虑聚类中心初始化和模糊集权 重的问题,造成聚类效果不稳定和无法全面利用评价信息的问题,影响推荐精度。针对上述问题,提出了 一种融合ROUSTIDA 和改进的概率直觉模糊聚类的协同过滤推荐算法。该算法基于粗糙集理论中的属 性约简规则,并以缺失矩阵与相似矩阵的差异最小为原则填补缺失数据,降低数据稀疏性,引入密度函数 初始化方法并完成聚类中心的选择,缓解模糊聚类对初始聚类中心的高敏感度,在聚类计算中分别求解隶 属度和非隶属度的概率权重和犹豫度相关系数,以添加权重的概率欧氏距离作为聚类的邻近函数以筛选 出相关邻居集合,在聚类过程中保留了更多的用户评价信息。在MovieLens 100K 和Jester数据集上的 实验结果显示,相较于UFCM 与FCM-Slope One等其他基于模糊聚类的推荐算法,所提算法的MAE 与 RMSE 指标更低,有更好的推荐精度。. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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