Elsevier

Information Sciences

Volume 235, 20 June 2013, Pages 117-129
Information Sciences

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

https://doi.org/10.1016/j.ins.2013.01.025Get rights and content

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.

Introduction

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], [4], [5], [6], [7], [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”.

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.

Section snippets

Background and related works

In this section, a review of web personalization and its application is first presented. We then provide an overview of recommender systems as well as the principal hybrid recommendation algorithms. Finally, we outline the current development of recommender systems using fuzzy techniques to handle uncertainty.

Fuzzy techniques preliminaries

For the description of the proposed approach, based on Zadeh [35], we first introduce some basic notions of fuzzy sets, fuzzy numbers, positive and negative fuzzy numbers, linguistic variables etc., and give related theorems [36]. These notions are used in a linguistic term similarity calculation in the proposed recommendation approach and the FTCP-RS software.

Definition 1

A fuzzy set A˜ in a universe of discourse X is characterized by a membership function μA˜(x) which associates with each element x in X a

Approach description

To deal with the sparsity problems and improve the prediction accuracy, particularly to handle customer data uncertainty and fully use business knowledge in recommendation, this study develops an approach which integrates Item-based Collaborative Filtering (IBCF) and User-based Collaborative Filtering (UBCF) with fuzzy set techniques and knowledge-based method (business rules). It first uses IBCF to produce predictions to form a dense user-item rating matrix, and based on this matrix, UBCF is

Experimental results

Before implementing the proposed approach in an online system, FTCP-RS, and applying it to a real world telecom application, we conducted a set of experiments using the MovieLens 100 K dataset accessed from the GroupLens Research website (http://www.grouplens.org/node/73) to test the prediction accuracy of the proposed hybrid recommendation approach.

System architecture and development

This section describes the development of a Fuzzy-based Telecom Product Recommender System (FTCP-RS).

System application

The main process of recommendation in the use of FTCP-RS is described as follows:

  • (1)

    To collect customer information. In this step, the rating data of customers are collected in the mobile product/service and handset detail web pages on which a customer can rate a mobile product/service and a handset. The rating value, as well as the customer ID and mobile product/service ID or handset ID, will then be stored in the database.

  • (2)

    To gather data from similar existing customers, including purchase

Final discussion and further study

This study proposes a hybrid recommendation approach which combines user-based and item-based collaborative filtering techniques with fuzzy set techniques and knowledge base for mobile product and service recommendation. It particularly implements the approach in a personalized recommender system for telecom products/services called FTCP-RS. This system has undergone preliminarily testing in a telecom company and achieved excellent performance.

As we have mentioned in Section 1, telecom

Acknowledgements

The work presented in this paper was partially supported by the Australian Research Council (ARC) under discovery Grant DP110103733. The authors thank William Wang and Tai Zhang for their contributions in the development and implementation of the FTCP-RS.

References (39)

  • L.A. Zadeh

    Fuzzy sets

    Information and Control

    (1965)
  • L.A. Zadeh

    The concept of a linguistic variable and its application to approximate reasoning—I

    Information Sciences

    (1975)
  • N. Manouselis et al.

    Analysis and classification of multi-criteria recommender systems

    World Wide Web

    (2007)
  • X. Guo et al.

    Intelligent e-government services with personalized recommendation techniques

    International Journal of Intelligent Systems

    (2007)
  • E. Herrera-Viedma, C. Porcel, A. López-Herrera et al., A fuzzy linguistic recommender system to advice research...
  • D.N. Kanellopoulos

    An ontology-based system for intelligent matching of travellers’ needs for group package tours

    International Journal of Digital Culture and Electronic Tourism

    (2008)
  • K. Wei, J. Huang, S. Fu, A survey of e-commerce recommender systems, in: Proceedings of the IEEE International...
  • W. IJntema, F. Goossen, F. Frasincar et al., Ontology-based news recommendation, in: Proceedings of the 2010 EDBT/ICDT...
  • Cited by (0)

    View full text