Abstract:

Aiming at the problem of data sparsity and time effects in the traditional collaborative filtering system,a Collaborative Filtering Recommendation Algorithm with TimeAdjusting Based on Cloud Model (CTCFR) is proposed.It creates the user's preference of items' attributes by using the cloud model,and adjusts the items rating similarity by establishing an exponential time function.Based on the data set from GroupLens project team,the experimental result shows that this algorithm can make the measurement of the items rating similarity more accurate and improve the quality of the recommendation better.

Key words: the nearest neighbor collaborative filtering;cloud model;items rating similarity;timeadjusting