A Novel Recommender System Based on Fuzzy Set and Rough Set Theory

Recommender System is an effective means of handling information overload and can provide personalized service as a useful information tool in e-commerce. In this paper, a novel automatic recommender system is proposed based on fuzzy c-means algorithm and rough set theory, including three main steps: data discretization, rules establishing and fuzzy reasoning. A method for fitting the results of FCM clustering is put forward by membership function and using it with attributes to achieve data discretization, in order to avoid the processing that each input data have to carry clustering operation for discretization. Using rough set theory, it can accelerate the speed of fuzzy reasoning through rules reduction with choosing closely relating attributes for commodity. Fuzzy reasoning method is also applied into commodity recommender system. Lastly, a case study conducted by the presented method is given, and the results show that this model can provide valuable advice for potential users and it is also an effective way for recommendation.

[1]  GoldbergDavid,et al.  Using collaborative filtering to weave an information tapestry , 1992 .

[2]  Jian Yin,et al.  Collaborative filtering recommendation based on fuzzy clustering of user preferences , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.

[3]  S. Roberts,et al.  Bayesian multivariate autoregressive models with structured priors , 2002 .

[4]  Gwo-Hshiung Tzeng,et al.  Rough set-based logics for multicriteria decision analysis , 2007, Eur. J. Oper. Res..

[5]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[6]  XIAOHUA Hu,et al.  LEARNING IN RELATIONAL DATABASES: A ROUGH SET APPROACH , 1995, Comput. Intell..

[7]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[8]  Philip S. Yu,et al.  Redefining Clustering for High-Dimensional Applications , 2002, IEEE Trans. Knowl. Data Eng..

[9]  Yaser E. Hawas.  A fuzzy-based system for incident detection in urban street networks , 2007 .

[10]  Gwo-Hshiung Tzeng,et al.  Decision Support Rough set-based logics for multicriteria decision analysis , 2007 .

[11]  Gang Wang,et al.  A Fuzzy Adaptive Multi-Population Parallel Genetic Algorithm for Spam Filtering , 2011 .

[12]  Alexander Tuzhilin,et al.  Towards the Next Generation of Recommender Systems , 2010, ICE-B 2010.

[13]  C. V. Ramamoorthy,et al.  Knowledge and Data Engineering , 1989, IEEE Trans. Knowl. Data Eng..

[14]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[15]  Shih-Jung Wu.  Fuzzy-Based Handover Decision Scheme for Next-Generation Heterogeneous Wireless Networks , 2011 .

[16]  John Riedl,et al.  Recommender systems in e-commerce , 1999, EC '99.