Collaborative filtering recommendation algorithm based on hybrid similarity

As the traditional collaborative filtering algorithm only relies on user ratings to calculate the similarity between users, and then find each user's K neighbors, finally recommend according to the K neighbor set. However, in the face of large data processing, the traditional collaborative filtering algorithm is sparsely populated, resulting in the recommendation is not obvious. A collaborative filtering algorithm for hybrid similarity is proposed for this problem. The algorithm is mainly focused on the fusion of the user rating similarity and social similarity, so as to make recommendation more suitable for the user and improve the recommended quality of the algorithm. The experimental results show that the method proposed in this paper has lower MAE value than the traditional cooperative filtering algorithm and improves the recommended quality.