A hybrid fuzzy-based Personalized Recommender System for Telecom Products/Services

Download 119.17 Kb.
Size119.17 Kb.
  1   2   3   4   5   6

A hybrid fuzzy-based Personalized Recommender System for Telecom Products/Services

Zui Zhang, Hua Lin, Kun Liu, Dianshuang Wu, Guangquan Zhang, Jie Lu

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, Broadway, NSW 2007, Australia

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.

Keywords: Recommender systems, telecom products/services, web personalization, collaborative filtering, fuzzy sets


Telecom businesses today offer hundreds of different mobile products and services such as handsets, mobile plans, prepaid mobiles, and broadband to customers and are constantly exploring new service models that will support customers in their selection and purchase of products and services on the Internet. Telecom products are always linked with services, referred to hereafter as ‘products/services’, and have very complex structures and a huge number of choices. For example, a telecom company may have more than 500 telecom products/services in several categories for different groups of customers (individual consumers, small businesses, medium businesses and large businesses). With such a vast number of choices, it is becoming increasingly difficult for customers to find their favourite products quickly and accurately. Only experienced salespeople in a telecom company can make suitable personalized recommendations to customers, which is costly and inefficient. To help customers shop online, telecom businesses need to develop web-based intelligent information technologies that will fully use salespeople’s knowledge to help telecom customers select suitable products or services online.

Recommender systems are designed to resolve this problem by automatically making helpful recommendations about various products and services to customers [1]. Such systems can make recommendations according to user profiles or preferences, or they can rely on the choices of other people who could be useful referees. The advantage of recommender systems is that they suggest the right items (products or services) to particular users (customers, suppliers, salespeople, etc.) based on their explicit and implicit preferences by applying information filtering technologies [2]. In recent years, significant steps have been taken towards providing personalized services for a wide variety of web-based applications in e-commerce, e-business, e-learning and e-government [3-8]. Successful applications using recommendation techniques have involved various product and service areas such as recommending news, movies, books, videos, exhibitions, and business partners [9, 10].

This study aims to build a Web-based Product/Service Online Recommender System to support telecom companies in guiding customers in the selection of the most appropriate telecom products/services, which is an important part of telecom customer relationship management and business intelligence. Note that in this paper, we focus only on individual consumers, not business customers. This system we propose can automatically predict the behaviour and requirements of customers based on existing customers’ profiles and business knowledge. It can be used by customer-care office and salespeople in telecom businesses as well as online telecom shops to generate recommendations to customers of the most appropriate products/services.

There are three main difficulties in telecom product/service recommendation compared to other industries. Firstly, telecom products/services have very complex descriptions and features. A complete mobile product/service for a customer includes handsets and related mobile services. A mobile service is a specification of the available sub-services and related prices, discounts and rewards. It is represented by a set of attributes such as the monthly access fee, call rate, data charging, rewards, and so on. A mobile service is often combined with an Internet service. Table 1 shows a set of mobile products/services which illustrates the complexity of telecom products/services. Secondly, mobile services and handsets are updated frequently, but a mobile customer has one product at a time. These two features result in a lack of rating information on products from customers, which creates difficulties for making comparisons between telecom products/services and generating recommendations. Thirdly, telecom products change frequently, but some new products and old products have similarities. Also, telecom customers often express their preferences and interests in online products/services evaluations using linguistic terms, such as “good”, “very good”, and “interested”.

Table 1. Examples of Mobile Products/Services

Product/service Name

Telecom Product/service Description

X-Smart $70 Data100MB

$70 included value; 100MB Data Unlimited access within Australia to Facebook, Twitter, LinkedIn, MySpace, eBay and Foursquare within Australia

A-Smart Data 1.5GB

$55 included value; 1.5GB Data Unlimited access within Australia to Facebook, Twitter, LinkedIn, MySpace, eBay and Foursquare within Australia

Smart Data 2GB 24M

$75 included value1; 2GB Data Unlimited standard SMS to Australian GSM mobiles; Unlimited access within Australia to Facebook, Twitter, LinkedIn, MySpace, eBay and Foursquare within Australia


Unlimited included value; 5GB Data Unlimited standard SMS and MMS to Australian GSM mobiles (excl. Pivotel); Unlimited access within Australia to Facebook, Twitter, LinkedIn, MySpace, eBay and Foursquare within Australia


Unlimited included value; 6GB Data Unlimited standard SMS and MMS to Australian GSM mobiles (excl. Pivotel); Unlimited access within Australia to Facebook, Twitter, LinkedIn, MySpace, eBay and Foursquare within Australia

X’Data $19.99


To deal with the above difficulties and help a customer to choose the most appropriate telecom products/services, this paper considers both customer similarity and product similarity in recommendation generation. Because the similarity between products/services or between users is naturally uncertain, fuzzy set theory lends itself well to handling the fuzziness and uncertain issues in recommendation problems [11]. More importantly, fuzzy set techniques can be applied to tackle linguistic variables, which are used in describing customer preference, and have the ability to support recommendation generation using uncertain information.

The main contribution of this study is the development and implementation of a personalized recommendation approach and a software system for telecom products/services recommendation that combines both item-based and user-based collaborative filtering methods with fuzzy set techniques and knowledge-based method (business rules), which we call a Fuzzy-based Telecom Product Recommender System (FTCP-RS). It explores a new area of recommender systems and telecom business intelligence.

The remainder of this paper is organized as follows. In Section 2, the research background and related work are expatiated. Section 3 describes related fuzzy set techniques. The recommendation approach is described in Section 4, and related experiments are shown in Section 5. In Section 6, we present the architecture and design steps of the FTCP-RS which implemented the proposed recommendation approach, and Section 7 illustrates an initial application of the proposed FTCP-RS. Finally, conclusions and future study are given in Section 8.

Directory: bitstream -> 10453
bitstream -> Amphitheater High School’s Outdoor Classroom: a study in the Application of Design
bitstream -> Ethics of Climate Change: Adopting an Empirical Approach to Moral Concern
bitstream -> The Age of Revolution in the Indian Ocean, Bay of Bengal and South China Sea: a maritime Perspective
bitstream -> Methodism and Culture
bitstream -> Review of coastal ecosystem management to improve the health and resilience of the Great Barrier Reef World Heritage Area
bitstream -> Present state of the area
10453 -> Managing Olympic Venues Simon Darcy & Tracy Taylor
10453 -> Implementing Digital Media Presentations as Assessment Tools for Pharmacology Students Jorge Reyna*, Peter Meier, Francis Geronimo and Kenneth Rodgers
10453 -> Perceptions of the Hospital School Experience: Implications for Pedagogy and the use of Technology
10453 -> Sex and the Virtual Suburbs: The Pornosphere and Community Standards. Alan McKee1, Brian McNair, Anne-Frances Watson Introduction: From physical spaces to virtual spaces

Download 119.17 Kb.

Share with your friends:
  1   2   3   4   5   6

The database is protected by copyright ©ininet.org 2024
send message

    Main page