Traditional collaborative filtering algorithm calculates the difference of scores only for the common items of users while calculating the similarity of users. Owing that the numbers of common items of different users is not the same, the recommendation quality is not reliable. We proposed a new algorithm, taking both the number of common items and the popularity of goods into consideration while calculating the similarity of users. Experimental results show that, the recommendation quality of new algorithm is improved by more than one time than traditional algorithm in both precision and recall. In addition, results also show that using Pearson correlation as similarity metric obtained higher recommendation quality than Euclidean distance. 