An Hybrid Similarity Function for Neighbor Selection in Collaborative Filtering

Collaborative Filtering, which plays an important role in the recommendation, is based on the way that users rate the items throughout history. Although the CF recommendation system is used widely, the performance of recommendation still needs improving. In order to improve the accuracy of CF, the weight of item and the factor of time are considered in this paper. A new similarity method is proposed by improving the traditional similarity function with the weight of items. When putting the similarity of items and the factor of time as the weight of the target item, the neighbors of a user is not identical for all his items. Experimental results from MAE, precision, recall, f1 represent that the algorithm proposed can improve the performance of recommendation system. Keyword: collaborative filtering; similarity calculation; weight of item; time factor