协同过滤技术在个性化推荐中的运用

Research on collaborative filtering in personality recommendation systems

协同过滤技术是目前运用最广泛的个性化推荐技术之一,但随着系统规模的不断扩大,用户评分数据极端稀疏等问题使其推荐质量严重下降。因此,文章提出将维数简化和聚类的方法运用到协同过滤技术中,从而较好地解决协同过滤推荐技术中存在的稀疏性、扩展性等问题,快速准确地产生个性化推荐结果。

Collaborative filtering is one of the most widely used technologies in personality recommendation. However, the efficiency of this technology declines by the increasing number of users and items, which results in extremely sparse data of users＇ assessments and other problems. In the paper, the methods of dimensionality reduction and clustering are proposed,which may solve the problems of sparsity and scalability, so that accurate results of personality recommendation can be obtained quickly.