Collaborative filtering recommendation algorithm based on user clustering of item attributes

Collaborative filtering recommendation algorithm is key technologies of personalized recommendation system,as the serious sparsity data of rated items,the similar users of active user is distribution of scattered. The traditional collaborative filtering recommender system algorithm consumes too many resources to search the nearest neighbor,as well as reliability is poor. A novel collaborative filtering recommender algorithm based on user clustering of item attributes is proposed. This algorithm reduces the negative effect on quality of recommendation,and shrinks the searching scope,which improves recommendation results for the system.