A Fuzzy Logic Based Personalized Recommender System

The ever-increasing number of E-commerce sites on the Internet has brought about information overload. This has made it difficult for consumers of certain products to find information about such products in an attempt to purchase products that best satisfies them. It has equally reduced the volume of product sales in the E-commerce domain. Hence, this paper proposes a personalized recommender system driven by fuzzy logic technique. The proposed system intelligently mines information about the features of laptop computers and provides professional services to potential buyers by recommending optimal products based on their personal needs. Fuzzy Near Compactness (FNC) concept is employed to measure the similarity between consumer needs and product features in order to recommend optimal products to potential buyers. Experimental result of the proposed system with 50 laptop computers consisting of Acer, Dell, HP, Sony, and Toshiba proves its effectiveness. Keywords-Fuzzy logic; Laptops; Recommendation system; Attributes; E-Commerce; Mining.

[1]  L. Kuncheva,et al.  Fuzzy diagnosis , 1999, Artif. Intell. Medicine.

[2]  Pabitra Mitra,et al.  Feature weighting in content based recommendation system using social network analysis , 2008, WWW.

[3]  Rudolf Kruse,et al.  Obtaining interpretable fuzzy classification rules from medical data , 1999, Artif. Intell. Medicine.

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

[5]  Minghe Huang,et al.  A Personalized Recommendation System Based on Multi-agent , 2008, 2008 Second International Conference on Genetic and Evolutionary Computing.

[6]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[7]  A. M. Madni,et al.  Recommender systems in e-commerce , 2014, 2014 World Automation Congress (WAC).

[8]  Paul Jen-Hwa Hu,et al.  A Web-based personalized recommendation system for mobile phone selection: Design, implementation, and evaluation , 2010, Expert Syst. Appl..

[9]  Shih-Wen Hsiao,et al.  Evaluation of alternatives for product customization using fuzzy logic , 2004, Inf. Sci..

[10]  Dragan Peraković,et al.  Model for Classification and Selection Mobile Terminal Devices Applying Fuzzy Logic , 2011 .

[11]  Lakhmi C. Jain,et al.  Introduction to fuzzy systems , 1995, Proceedings Electronic Technology Directions to the Year 2000.

[12]  Hao Ying Fuzzy Systems Technology: a Brief Overview , .

[13]  M. Neshat,et al.  A Fuzzy Expert System for Heart Disease Diagnosis , 2022 .

[14]  Sung-Shun Weng,et al.  Personalized product recommendation in e-commerce , 2004, IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE '04. 2004.

[15]  B. Joseph Pine,et al.  The Experience Economy , 2020, Journal of Orthopaedic Experience & Innovation.

[16]  Robin Burke,et al.  Knowledge-based recommender systems , 2000 .

[17]  Jiuh-Biing Sheu,et al.  A fuzzy-based customer classification method for demand-responsive logistical distribution operations , 2003, Fuzzy Sets Syst..

[18]  Yung-Ming Li,et al.  TREPPS: A Trust-based Recommender System for Peer Production Services , 2009, Expert Syst. Appl..