Abstract:

Since the useritem matrix is sparse, and there are less users or items satisfying the conditions, the precision of the algorithm can’t be high. By sampling neighbor collaborative filtering algorithms, users take full advantage of score matrix provided information to increase the users or projects participated in the calculation process, so as to solve the shortage of traditional collaborative filtering algorithms in real application. Experiment results show that the new algorithm can effectively improve the precision in recommendation along a small increasing of runtime.

Key words: collaborative filtering, sparse matrix, precision of recommendation, neighbor