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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 (IJACR), Volume-3, Issue-10, June-2013 ,pp.101-107. |