(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Computer Research (IJACR)

ISSN (Print):2249-7277    ISSN (Online):2277-7970
Volume-3 Issue-10 June-2013
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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.