计算机应用与软件Issue(10)：285-287,328,4.DOI:10.3969/j.issn.1000-386x.2014.10.069
综合用户偏好和优先新品推荐的协同过滤算法
A COLLABORATIVE FILTERING ALGORITHM INCORPORATING USERS PREFERENCE AND RECOMMENDING NEW PRODUCTS FIRST
摘要
Abstract
Collaborative filtering algorithm is one of the most important technologies in e-commerce recommendation system,but tradition-basedcollaborative filtering recommendation technology does not take into account the new goods that have more recommendation values.In this paper we propose an improved strategy,which uses matrix decomposition SVD algorithm and cosine similarity to group users clustering with common interests and to extract the eigenvector of products to be evaluated by the users in group.By using BP neural network for training,it predicts the satisfaction of users'group on unknown products.For those satisfied new products it assigns higher recommending grade,and gives the priority to recommending them.At the same time,the strategy effectively makes up the problems of cold start and sparse matrix,etc.commonly existed in traditional collaborative filtering algorithms.关键词
协同过滤/矩阵分解SVD/余弦相似性/BP神经网络Key words
Collaborative filtering/Matrix decomposition SVD/Cosine similarity/BP neural networks分类
信息技术与安全科学引用本文复制引用
吴杰,冯锋..综合用户偏好和优先新品推荐的协同过滤算法[J].计算机应用与软件,2014,(10):285-287,328,4.基金项目
宁夏自然科学基金重点项目（NZ13004）。 （）
