A research of collaborative filtering recommendation based on ant colony algorithm

Imitated ant foraging theory, users are regarded as different attributes ants, clustering centers are regarded as the “food source” that ants search, proposed to realize user clustering based on ant algorithm for improving the query speed of nearest neighbors in the collaborative filtering recommendation system, reducing the cost of the search, and avoiding the effect of initial clustering centers and clustering numbers in the use of K-Means clustering method. Finally, the experiment verify that user clustering through ant colony algorithm is effective, and solve the problem of new user that is not recommended, enhance the precision of collaboration filtering recommendation algorithm.