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

1 INTRODUCTION

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”.

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.