User Attributes Clustering Based Collaborative Filtering Recommendation Algorithm and Its Parallelization on Spark
Personalized recommendation system is an important means for people to get interested information and product quickly. This traditional user based collaborative filtering algorithm cost too much computation on similarity calculation. In order to solve this problem, a new collaborative filtering recommendation algorithm based on K Means clustering of user's attributes is proposed. In this algorithm, the longitude and latitude of users' are first clustered, and then the similarity of users' are calculated within each cluster. Finally, parallelization of this proposed algorithm on Spark is implemented. Experiments show that the user attributes based collaborative filtering has satisfied performance.
AsiaSim/SCS AutumnSim