A hybrid fuzzy-based personalized recommender system for telecom products/services

  • Authors:
  • Zui Zhang;Hua Lin;Kun Liu;Dianshuang Wu;Guangquan Zhang;Jie Lu

  • Affiliations:
  • Decision Systems & e-Service Intelligence Lab, Centre for Quantum Computation & Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, PO Box 123 ...;Decision Systems & e-Service Intelligence Lab, Centre for Quantum Computation & Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, PO Box 123 ...;Decision Systems & e-Service Intelligence Lab, Centre for Quantum Computation & Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, PO Box 123 ...;Decision Systems & e-Service Intelligence Lab, Centre for Quantum Computation & Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, PO Box 123 ...;Decision Systems & e-Service Intelligence Lab, Centre for Quantum Computation & Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, PO Box 123 ...;Decision Systems & e-Service Intelligence Lab, Centre for Quantum Computation & Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, PO Box 123 ...

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2013

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Abstract

The Internet creates excellent opportunities for businesses to provide personalized online services to their customers. Recommender systems are designed to automatically generate personalized suggestions of products/services to customers. Because various uncertainties exist within both product and customer data, it is a challenge to achieve high recommendation accuracy. This study develops a hybrid recommendation approach which combines user-based and item-based collaborative filtering techniques with fuzzy set techniques and applies it to mobile product and service recommendation. It particularly implements the proposed approach in an intelligent recommender system software called Fuzzy-based Telecom Product Recommender System (FTCP-RS). Experimental results demonstrate the effectiveness of the proposed approach and the initial application shows that the FTCP-RS can effectively help customers to select the most suitable mobile products or services.