文章基本信息

标题：Reduction of Data Sparsity in Collaborative Filtering based on Fuzzy Inference Rules
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作者：Atisha Sachan ; Vineet Richhariya
期刊名称：International Journal of Advanced Computer Research
印刷版ISSN：2249-7277
电子版ISSN：2277-7970
出版年度：2013
卷号：3
期号：2
出版社：Association of Computer Communication Education for National Triumph (ACCENT)
摘要：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).
关键词：Collaborative Filtering; Sparsity; Accuracy; Fuzzy ;Inference Rule; MovieLens