Reduction of Data Sparsity in Collaborative Filtering based on Fuzzy Inference Rules

Collaborative filtering Recommender system plays a very demanding and significance role in this era of internet information and of course e commerce age. Collaborative filt ering predicts user preferences from past user behaviour or user - item relationships. Though it has many advantages it also has some limitations such as sparsity, scalability, accuracy, cold start problem etc. In this paper we proposed a method that helps in reducing sparsity to enhance recommendation accuracy. We d eveloped fuzzy inference rules which is easily to implement and also gives better result. A comparison experiment is also performing with two previo us methods, Traditional Collaborative Filtering (TCF) and Hybrid User Model Technique (HUMCF).