In order to solve the problem of data sparsity in the collaborative filtering algorithm, this paper proposed an improved collaborative filtering algorithm. The algorithm filled the unrated items in the scoring matrix using the prediction value calculated by the slope-one algorithm and then gave recommendations based on the user-based collaborative filtering method based on the filled user-item scoring matrix. Using the predictive value of slope-one algorithm as the backfill value could not only reduce the sparsity of the scoring matrix, but also ensured the diversity of backfill values, so as to reduce the recommended error caused by the single fill value such as mean value and median value. It performed half off cross-validation experiments on the MovieLens-1M dataset. The results show that the collaborative filtering algorithm based on the score prediction data effectively mitigates data sparsity and has better performance recommended effect.

[2019-04-05] Printed Article /doc/2017.12.0751/v2