缺失数据下基于SVDIFC的协同过滤推荐算法  

Collaborative filtering recommendation algorithm based on SVDIFC under missing data

作　　者：纪成君[1] 李蕊 王仕勤 Ji Chengjun;Li Rui;Wang Shiqin(School of Business Administration,Liaoning Technical University,Huludao Liaoning 125105,China)

机构地区：[1]辽宁工程技术大学工商管理学院,辽宁葫芦岛125105

出　　处：《计算机应用研究》2021年第10期2994-2999,共6页Application Research of Computers

摘　　要：为了提高个性化推荐的准确性和质量,针对传统推荐算法的信息过载和数据稀疏性问题,构建了基于SVD与直觉模糊聚类的协同过滤推荐算法(SVDIFC-CF)。算法首先引入SVD将降维后的原始矩阵进行填充;再运用用户商品喜好矩阵将用户进行直觉模糊聚类;最后计算与目标用户相似度最高的前N个用户,找到用户最感兴趣的项目作为推荐结果。采用MovieLens与Jester数据集对算法的有效性进行验证,实验结果表明相对于传统推荐算法,该算法能有效解决数据稀疏和冷启动问题,提高推荐精度与质量。In order to improve the accuracy and quality of the personalized recommendation system,this paper constructed a collaborative filtering recommendation algorithm based on SVD and intuitionistic fuzzy clustering(SVDIFC-CF)to address the information overload and data sparsity problems in traditional recommendation algorithms.The algorithm firstly introduced SVD to fill in the original matrix after dimensionality reduction.Then it used the user-item preference matrix to perform intuitionistic fuzzy clustering of users.Finally it calculated the top N users with the highest similarity to the target user,and found the items that users were most interested in as recommendation results.This paper used the MovieLens and Jester datasets to verify the effectiveness of the algorithm.The experimental results show that compared with the traditional recommendation algorithm,this algorithm can effectively solve the problem of data sparseness and cold start,and improve the accuracy and quality of recommendation.

关 键 词：奇异值分解 直觉模糊聚类 协同过滤 相似度 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]