摘要
现有的电影推荐算法中,协同过滤算法是最常使用、操作最简单方便的算法,但传统的协同过滤算法存在评分矩阵稀疏、推荐精度低等问题。针对这些问题,提出了矩阵填充策略,根据矩阵填充技术的优缺点,选择了几种填充稀疏矩阵的方法,并且利用迭代SVD算法得到了电影推荐的局部最优解,并利用均方根误差(RMSE)对结果进行了评价,利用R软件对电影评分数据集进行处理,实验结果表明,与传统的协同过滤推荐算法相比,迭代SVD算法能有效地提高推荐的准确性,更加准确地给用户提供想看的电影。
Among the existing film recommendation algorithms,collaborative filtering algorithm is the most commonly used and easy to operate algorithm,but the traditional collaborative filtering algorithm has the problems of sparse rating matrix and low recommen⁃dation accuracy.According to the advantages and disadvantages of matrix filling technology,several methods of filling sparse ma⁃trix are selected.The local optimal solution of movie recommendation is obtained by using iterative SVD algorithm,and the results are evaluated by root mean square error(RMSE).Finally,R software is used to process the movie rating data set.The experimental results show that the proposed method is more effective than the traditional method Compared with the collaborative filtering recom⁃mendation algorithm based on SVD,the iterative SVD algorithm can effectively improve the accuracy of recommendation and pro⁃vide users with more accurate movies they want to see.
作者
武文硕
左安
WU Wen-shuo;ZUO An(School of Statistics and Information,Shanghai University of International Business and Economics,Shanghai 201620,China)
出处
《电脑知识与技术》
2021年第15期1-3,共3页
Computer Knowledge and Technology
关键词
协同过滤
矩阵填充
稀疏矩阵
电影推荐
collaborative filtering
matrix filling
sparse matrix
movie recommendation
