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
Copyright
c
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
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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.
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