Paper Title:
Reduction of Data Sparsity in Collaborative Filtering based on Fuzzy Inference Rules
Author Name:
Atisha Sachan, Vineet Richhariya
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).
Keywords:
Collaborative Filtering, Sparsity, Accuracy, Fuzzy Inference Rule, MovieLens.
Cite this article:
Atisha Sachan, Vineet Richhariya. Reduction of Data Sparsity in Collaborative Filtering based on Fuzzy Inference Rules. International Journal of Advanced Computer Research. 2013;3(10):101-107. DOI :