Collaborative filtering is one of the most successful recommendation technologies

but the data sparsity results in low recommendation accuracy and poor efficiency. So an improved weighted Slope one and collaborative filtering algorithm is proposed. Based on users’ ratings

it calculates the similarity between users

so that to find user’s the nearest neighbors. Based on the score of user’s nearest neighbors

user-based collaborative filtering and weighted Slope one algorithm is used to predict the unknown rating of the target users and to present recommendation results. In the experiment

Movie Lens data set is used as test data. The experimental results suggest that the improved algorithm improves prediction accuracy and recommendation performance.