Improved Collaborative Filtering Algorithm (ICF)

Collaborative filtering recommendation currently serves as a key method when it comes to how to rapidly and effectively make proper personalized recommendation for customers. For the sake of above, it becomes a research hot pot in e-commerce system. By analyzing the defects of existing collaborative filtering algorithm, we proposed an improved collaborative filtering algorithm (ICF). In our construction, we firstly establish "users-items" interest degree matrix, and introduce the mechanism of singularity degree to generate the set of similar users, and then optimizes the method of neighbor set generation by recommend importance mechanism. Finally, our experiment proves that ICF has higher performance of accuracy and coverage than other existing algorithms.