Effective Similarity Measures of Collaborative Filtering Recommendations Based on User Ratings Habits

Abstract

The core of the recommendation system is the recommendation algorithm, especially the application of collaborative filtering recommendation algorithm is the most widely used. With the rapid increase of data sparsity. This paper aims at the problem of data sparsity in collaborative filtering algorithms. By mining the hidden information behind the user and the project, that is, considering different factors in the user's personal rating habits, and using Cosine and Jaccard to calculate the full degree of similarity to effectively use the rate data, improves the similarity calculation method, and solves the problem of low accuracy of the recommendation due to inaccuracy of similarity calculation. This is more in line with the logic of real life and can produce reasonable recommendations.