基于模糊聚类与用户兴趣的协同过滤推荐算法

Collaborative Filtering Recommendation Algorithm Based on Fuzzy Clustering and User Interest

针对传统协同过滤推荐算法存在的数据稀疏、推荐效率低等问题,提出一种基于模糊聚类与用户兴趣的协同过滤推荐算法。首先,分别构建用户—物品评分矩阵和用户—兴趣偏好矩阵;其次,使用粒子群优化的模糊聚类算法对用户兴趣进行聚类,将具有相似偏好的用户对该物品评分的平均值填充用户—物品评分矩阵,缓解了用户数据的稀疏性;最后,该算法综合考虑了用户—物品评分矩阵以及用户—兴趣偏好矩阵计算用户相似度,并引入物品类型惩罚因子,进一步改善用户偏好的准确度。实验结果表明,该算法能有效缓解数据的稀疏性,提高推荐算法的准确度。

Aiming at the problems of sparse data and low recommendation efficiency in traditional collaborative filtering recommendation algo-rithms,this paper proposes a collaborative filtering recommendation algorithm based on fuzzy clustering and user interest.Firstly,the user-item rating matrix and user-interest preference matrix are constructed respectively.Secondly,the fuzzy clustering algorithm based on particle swarm optimization is used to cluster user interests,and the average score of the item by users with similar preferences is filled into the user-item scoring matrix,which alleviates the sparsity of user data.Finally,the algorithm comprehensively considers the user-item rating matrix and user-interest preference matrix to calculate user similarity,and introduces the item type penalty factor to further improve the accuracy of user preference.Experimental results show that the proposed algorithm can effectively alleviate the sparsity of data and improve the accuracy of the recommendation algorithm.