基于SVD的协同过滤电影推荐算法

Collaborative filtering movie recommendation algorithm based on SVD

信息过载的问题愈发严重,在大数据时代针对不同用户提高电影推荐系统的推荐性能一直存在巨大的挑战。为了有效地解决信息过载和用户体验满意度低的问题,需要选择合适的个性化推荐算法。文章概述了主流的机器学习推荐算法,并通过实验比较分析了各算法的优缺点,针对推荐算法普遍存在的冷启动和数据稀疏性问题,提出了相应的解决方案。

Nowadays.the problem of information overload is becoming more and more serious.In the sea of big data,it has been a huge challenge to improve the recommendation performance of movire commendation systetn for different users.In order to effectively solve the problems of informationgverload and low user experience satisfaction,it is necessary to choase an appropriate personalized recommendation algorithm.In this paper,the mainstream machine learning recommendation algorithms are summarized,and the advantages and disadvantagesof cach algorithm arecompared and analyzed thraugh experiments Finslly,cerresponding solutions are propesed to solve the common problems of cold start and data sparsity in recommendation algorithms.