A PERSONALIZED RECOMMENDER SYSTEM

FOR TELECOM PRODUCTS AND SERVICES

Zui Zhang, Kun Liu, William Wang, Tai Zhang and Jie Lu

Decision Systems & e-Service Intelligence Lab, Centre for Quantum Computation & Intelligent Systems

Faculty of Engineering and Information Technology, University of Technology

PO Box 123, Broadway, NSW 2007, Sydney, Australia

Keywords: Recommender systems, Telecom products and services, Web personalization, Collaborative filtering.

Abstract: The Internet brings excellent opportunities to businesses for providing personalized online services to their

customers. Recommender systems are designed to automatically generate personalized recommendations of

products and services. This study develops a hybrid recommendation approach which combines user-based

and item-based collaborative filtering techniques for mobile product and service recommendation. It

particularly implements the approach into an intelligent recommendation system called telecom product

recommender system (TCPRS). Experimental results show that the TCPRS can effectively help new

customer selecting the most suitable mobile products and services.

1 INTRODUCTION

Today, telecom businesses offer hundreds of

different mobile products and services to customers

and are exploring new service models such as to

allow customers to select and purchase

products/services through Internet. However, with

such a vast number of choices, it is becoming more

and more difficult for customers to find its favourite

choice fast and accurately. Recommender systems

are designed to help customers resolve this problem

by automatically giving them helpful

recommendations of various products and services.

The systems can give recommendations according to

user profiles or preferences, or rely on the choices of

other people who could be useful references. The

advantage of recommender systems is to suggest the

right items (products or services) to particular user

(customers, suppliers, etc) based on their explicit

and implicit preferences by applying information

filtering technology (Adomavicius and Tuzhilin

2005). In recent years, significant steps have been

taken in the direction of providing personalized

services for a wide variety of web-based applications

in e-commerce, e-learning, and e-government (Guo

and Lu 2007). The successful applications of

recommendation techniques have involved various

product and service areas such as recommending

news, movies, books, videos, exhibitions, and

business partners (Lu et al., 2010).

This study explores a new area of recommender

systems to support customers selecting the most

appropriate mobile products/services online. The

main contribution of this study is the development

and implementation of a personalized

recommendation system for telecom products/

services by combining both item-based and user-

based collaborative filtering methods, called

TeleCom Product Recommender System (TCPRS).

The main process of recommendation by using

TCPRS can be described as follows: 1) to collect

new customer information; 2) to gather similar

existing customers’ data, which includes purchase

records, website visit history as well as personal

profiles; 3) to collect related product data; 4) to

analyses, the collected data (customers and the

products) and predict the ratings of unrated products;

5) to select top-N products with highest predicted

ratings as recommendations to the customers.

The rest of this paper is organized as follows.

Section 2 discusses related research in recommender

systems. In Section 3, we present a personalized

recommendation system for telecom product and

services, TCPRS, using a hybrid approach that

combines item-based and user-based collaborative

filtering methods. A thorough analysis of the

performance of the TCPRS is shown in Section 4.

689

Zhang Z., Liu K., Wang W., Zhang T. and Lu J..

A PERSONALIZED RECOMMENDER SYSTEM FOR TELECOM PRODUCTS AND SERVICES .

DOI: 10.5220/0003288106890693

In Proceedings of the 3rd International Conference on Agents and Artiﬁcial Intelligence (ICAART-2011), pages 689-693

ISBN: 978-989-8425-40-9

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Section 5 demonstrates the implementation and

application of the TCPRS. Finally, conclusions and

future studies are discussed in Section 6.

2 RELATED WORKS

Recommender systems use the concept of rating to

measure how much a particular item is liked by the

target user. Adomavicius and Tuzhilin, (2005) stated

that in middle 1990s researchers in the

recommendation areas started to do research

focusing on ratings structure, and the problem of

recommendation has been simplified to be the

problem of predicting ratings of items that have not

been known by a user. In the literature of this field,

content-based (CB) methods and collaborative

filtering (CF) methods are the most popular

techniques adopted in recommender systems

(Iaquinta et al., 2007). The CB methods recommend

products by comparing the content or profile of the

unknown products to those products that are

preferred by the users. Differ from CB methods, CF

methods do not involve user profiles and item

features when giving recommendation, but only rely

on the user ratings. A third approach is hybrid

methods which combine CB and CF methods and it

is becoming more popular among researchers in this

field. Iaquinta et al., (2007) involved CB methods

into CF model for calculating user similarities using

user profiles, which are built using machine learning

techniques. Su et al., (2007) built a model using

multiple experts including both CB and CF

approaches to take different strategies in different

situations. All these methods are based on the rating

structure. In order to increase the accuracy or

performance of recommender systems, many

researchers have tried with some non-ratings

techniques, such as data mining, machine learning

and intelligent agents depending on different

circumstances. In this paper, we will only focus on

the algorithms and applications of collaborative

filtering methods.

There are several kinds of CF methods, among

them the most popular approaches are user-based CF

and item-base CF. A user-based CF method is to use

the ratings of users those are most similar to the

target user for predicting the ratings of unrated

items. On the other hand, item-based CF method

uses the similarities of items for predicting ratings.

Literature shows that the current trend of

recommender system is to combine two or more

techniques together for improving the accuracy of

recommendation or overcome the limitations of

single recommender algorithm, and the combination

of user-based CF and item-base CF may achieve a

good performance in a big-user-set and big-item-set

environment.

Therefore, this study implements a personalized

recommendation system TCPRS for telecom

products/services using a hybrid approach that

combines item-based and user-based CF methods.

3 ALGORITHM DESCRIPTION

Based on literature review (Shi et al., 2008), an

algorithm which integrates Item-based and User-

based Collaborative Filtering is designed to the

TCPRS. This algorithm takes advantage of both the

horizontal and vertical information in the user-item

rating table. The algorithm is described in seven

steps as follows.

1) Generate a User-item Rating Table: Each user

is represented by a set of item-rating pairs and the

summary of all those pairs can be collected into a

user-item rating matrix.

2) Calculate Item Similarity: This step measures

the similarities between any two items. Pearson

correlation is selected for this step which measures

the similarity between two items by calculating the

linear correlation between the two vectors.

3) Item Neighbours Selection: In most CF

methods, a number of neighbours will be selected

when predicting ratings. In the TCPRS, we used the

top-N technique for neighbour selection.

4) Predict Empty Ratings using Item-based CF: In

this step, all the unrated ratings can be calculated

using item-based CF method and all the empty cells

in the user-item rating table will be filled.

5) Calculate User Similarity: Beside from

predicting the ratings based on the similarities of

items, we can also predict the ratings by analysis the

similarities between users. We also use the Pearson

correlation algorithm for calculating the user

similarity.

6) Select Top-N Similar Users

: Similar as step 3,

we need to select a number of neighbour users for

predicting ratings. The Top-N technique is used in

the TCPRS system.

7) Final Recommendation Generation: The final

step of the algorithm is to predict the ratings of

every unrated telecomm product/services for the

target users using user-based CF. The new predicted

ratings will replace the ratings predicted in Step 4,

and be regarded as the final results. Based on the

ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

690

ratings, we could generate a set of most suitable

products/services for a user.

4 EXPERIMENTAL RESULTS

Before implement the approach discussed above into

an online system, we conducted a set of experiments

to validate the approach and evaluate its

performance. We used dataset provided by

Movielens for the experiment. Movielens is a movie

recommendation website that aims to support the

research and development of recommender systems.

It has been widely used by researchers around the

world for measuring the performance of

recommender systems.

4.1 Evaluation Metrics

There are several statistical accuracy metrics used

for comparing the predicted ratings with the user-

rated ratings methods, such as Mean Absolute Error

(MAE), Root Mean Squared Error (RMSE) and

Correlation. In the experiments, we select MAE to

be our evaluation method as it is easy to interpret

directly and very commonly used.

4.2 Experimental Analysis

In order to achieve accurate evaluation results, we

randomly select the training and testing datasets five

times so that we can have five training dataset

(u1base to u5base) and five testing dataset (u1test to

u5test). In order to measure the effect of the number

of neighbours on the accuracy of the algorithm, we

calculate the MAE four times separately using 5, 10,

20 and 50 neighbours for each training/testing

group. The testing result is as in Figure 1.

Figure 1: Experiment results.

From Figure 2(a), as only u1test has a slightly

higher average MAE than the rest, we can say that

the performance of the algorithm is quite uniform

across the dataset from Movielens. As shown in

Figure 2(b), the average MAE is falling while the

number of neighbour increases and this decrease is

most significant when the number of neighbours

increased from 5 to 10. Therefore, by considering

both the accuracy and calculation efficiency, we

decided that 10 neighbours is most proper for our

system.

(a)

(b)

Figure 2: (a) Average MAE of each group (b) Average

MAE of each different neighbour level.

4.3 Comparison Analysis

Su et al., (2007) carried out a series of experiments

for evaluating the accuracy of several different

recommender systems. They used the same

evaluation method and dataset as we did. Therefore,

their experiment results are very good references to

evaluate the performance of our algorithm. The

tested algorithms are: Pearson correlation-based CF

(PCCF), pure TAN-ELR model-based CF algorithm

(MCF), pure TAN-ELR content-based predictor

(CP), content-boosted CF (CBCF), and two hybrid

methods Sequential mixture CF (SMCF) and Joint

mixture CF (JMCF) introduced in this paper. The

experiment results are shown in Table 1.

A PERSONALIZED RECOMMENDER SYSTEM FOR TELECOM PRODUCTS AND SERVICES

691

Table 1: MAE result comparison of various CF

algorithms.

MAE

PCCF MCF CP CBCF SMCF JMCF

63.75

0.6901 0.7592 0.8055 0.6974 0.6820 0.6818

68.24

0.6976 0.7670 0.8203 0.7033 0.6883 0.6885

76.5

0.7108 0.7800 0.8178 0.7091 0.6932 0.6981

76.74

0.7325 0.8084 0.8359 0.7244 0.7088 0.7221

85.3

0.7723 0.8296 0.8479 0.7680 0.7155 0.7433

87.55

0.7895 0.8458 0.8664 0.7822 0.7303 0.7538

91.54

0.8166 0.8657 0.8952 0.7910 0.7416 0.7797

94.64

0.8937 0.8921 0.9178 0.8705 0.7785 0.8135

95.59

0.8858 0.8437 0.8669 0.8014 0.7335 0.7836

96.14

0.9803 0.9450 1.0174 0.8818 0.8200 0.8786

overall

0.7407 0.8000 0.8378 0.7344 0.7062 0.7188

For comparing with MAE of our algorithm with

their results, we believe the accuracy of our

algorithm is competitive with some other hybrid

recommender algorithms, and markedly higher than

traditional CB and CF recommender methods.

5 SYSTEM DEVELOPMENT

FOR TELECOM INDUSTRY

5.1 System Architecture

The TCPRS is development for telecom industry. It

is implemented using a Multi-Tiers architecture on

Microsoft .NET 3.5 platform. It consists of three

main parts: client, web server and database server.

• Client: Client is the user interface presented on

web browser. The client browser is responsible for

sending requests to web server every time the user

perform an action.

• Web Server: Websites are hosted in web servers.

It receives the requests from client browser, retrieves

the requested resources and sends them back to

client browser. Based on the web server, the TCPRS

web site can be divided into three layers, the

presentation layer, business logic layer and data

access layer. Presentation layer is responsible for

generating the requested web pages and handling the

UI logics and events. Business Logic layer defines

the business rules and processes of the application

(the CF recommender algorithm), and serves as a

mediator between the presentation layer and the data

access layer. Data Access Layer deals with the data

operations with database and transfers data with

business logic layer.

• Database Server: Database server is the server

runs the database applications. In TCPRS we use

SQL Server 2005 as the database application

because it is most compatible to Microsoft

technologies we used.

5.2 System Implementation

Data Collection: Data collection is the preliminary

work of the algorithm implementation. The rating

data of customers are collected in the handset details

web page where customer can rate a mobile phone.

Then the rating value, as well as the customer ID

and handset ID will be stored into database.

Handsets Recommendation: After customer logs

into its homepage, TCPRS is able to generate

recommendations to the customer. The system will

firstly read the algorithm settings from configuration

file and the rating records of the target user. If the

number of rating records is greater than the

threshold value, the system will use CF algorithm

for recommendation. Otherwise, the system will use

the hybrid method described in Section 3 for

recommendation. Finally, the system will return a

list of recommended handsets.

Plan Recommendation: The TCPRS can

recommend new plans (products in Telecom) and

extra packages (such as mobile phone service +

fixed line service) based on a customer’s average

usage in the last six months. The approach is to

calculate the actual cost of the average usage in

every possible plan and package combination, and

selects the one with the lowest cost to recommend to

the customer based on the business rules.

6 SUMMARY AND FURTHER

STUDY

In this paper, we present a personalized

recommendation system for telecom product and

services, TCPRS, which uses a hybrid approach that

combines both item-based and user-based CF

methods. This research is in progress. This system is

being used in a telecom company in Australia. In the

future, uncertainty data processing issue will be

considered to improve the proposed approach. The

approach will be also adopted to develop a mobile

service recommender system to support business

customers.

REFERENCES

Adomavicius, G. and Tuzhilin, A., 2005. Toward the Next

Generation of Recommender Systems: A Survey of

the State-of-the-Art and Possible Extensions, IEEE

ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

692

Transactions on Knowledge and Data Engineering,

vol. 17, no. 6, pp. 734 – 749.

Guo, X. and Lu, J., 2007. Intelligent E-Government

Services with Personalized Recommendation

Techniques, International Journal of Intelligent

Systems, vol. 22, no. 5, pp. 401-417.

Iaquinta, L., Gentile, A. L., Lops, P., de Gemmis, M. and

Semeraro, G., 2007. A Hybrid Content-Collaborative

Recommender System Integrated into an Electronic

Performance Support System, 7th International

Conference on Hybrid Intelligent Systems,

Kaiserlautern, pp. 47 – 52.

Lu, J., Shambour, Q., Xu, Y., Lin, Q. and Zhang, G., 2010.

Bizseeker: A Hybrid Semantic Recommendation

System for Personalized Government-to-Business E-

Services. Internet Research, 20(3):342-365.

Shi, X. Y., Ye, H. W. and Gong, S. J., 2008. A

Personalized Recommender Integrating Item-Based

and User-Based Collaborative Filtering, Business and

Information Management, International Seminar,

Wuhan, China, pp. 264 – 267.

Su, X. Y., Greiner, R., Khoshgoftaar, T. M. and Zhu, X.

Q., 2007. Hybrid Collaborative Filtering Algorithms

Using a Mixture of Experts, IEEE/WIC/ACM

International Conference on Web Intelligence,

Fremont, CA, pp. 645 – 649.

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