Collaborative Filtering Algorithm Based on User Clustering
Zhao Deng, Jin Wang · Applied Mechanics and Materials · 2013
To overcome the uncertainty of the users neighborhoods in the recommendation algorithm of nearest neighbor, an improved collaborative filtering algorithm based on user clustering is proposed. This improved algorithm filters the users by their features, and the improved cosine similarity algorithm is used for the item similarity computation. Experiments on the MovieLens dataset showed that, compared with Lis collaborative filtering algorithm, the recommendation quality of the improved algorithm is more accurate and the category coverage is larger.