Abstract

Collaborative filtering Recommender system plays a very demanding and significance role in this era of internet information and of course e commerce age. Collaborative filtering 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 developed fuzzy inference rules which is easily to implement and also gives better result. A comparison experiment is also performing with two previous methods, Traditional Collaborative Filtering (TCF) and Hybrid User Model Technique (HUMCF).

Keyword

Collaborative Filtering, Sparsity, Accuracy, Fuzzy Inference Rule, MovieLens.

Cite this article

.Reduction of Data Sparsity in Collaborative Filtering based on Fuzzy Inference Rules. International Journal of Advanced Computer Research. 2013;3(10):101-107.

Refference