Location Based Targeted Advertising Using Bayesian Network And Fuzzy TOPSIS

Arya Mazaheri, Nima Rafiee, Pejman Khadivi

Department of Electrical and Computer Engineering Isfahan University of Technology, 84156-83111, Isfahan, Iran

a.mazaheri@ec.iut.ac.ir, rafiee.nima@gmail.com, pkhadivi@ec.iut.ac.ir

Abstract— These days, with the rapid growth in mobile compu-ting and wireless communications, Location Based Services are being used more than ever. One of the most important issues in this field is providing the most appropriate content to users ac-cording to their interests and characteristics. However, in tradi-tional LBS systems, the location of the mobile subscriber was the only criteria, which has been used to determine whether to notify the user or not. In this paper we have proposed a new intelligent targeted advertising method in LBS environments based on Bayesian networks and Fuzzy TOPSIS. With the help of this me-thod we can provide an adaptive system to meet the user’s indi-vidual needs.

Keywords- Location Based Service (LBS); Targeted advertising; Bayesian Network; Fuzzy TOPSIS; Decision making

I. INTRODUCTION In the past years, rapid advances in the field of Location

Based Services (LBS) have emerged various issues. Although designing an appropriate LBS middleware is an important issue, providing the most suitable content is not negligible. Until a few years ago, Location Based Services have a simple procedure in order to publishing content to their clients: a mo-bile subscriber has sent a request to a remote server and then the server would return results to the user by considering the subscriber’s location [1]. Mobile navigation systems and yel-low page services are well-known examples of this model. As an example, mobile users request the location of the nearest restaurant and the LBS system directs them to the requested place [2].

However, with magnificent advances in mobile computing technologies, service providers began to develop intelligent LBS (iLBS) systems, which is based on push model or service initiated model [3]. In service initiated model, service provid-ers push the location-dependent information to mobile users by considering their predefined interests and also, their beha-viors [4]. There are various location based services which fall in this category. Mobile Buddy-List is an example, which is a kind of location aware messaging service [2].

In this paper, we utilize the iLBS to empower the targeted advertising industry. The effectiveness of targeting a small portion of customers for advertising has long been recognized by businesses [5] for two main reasons:

1. With the ever increasing amount of products and ser-vices presenting by various companies, we need to help the customers to find products/services they are looking for.

2. Recognizing the customers’ requirements and inter-ests is an essential part of customer-relationship management. Accurate identification of the customers’ individual needs and interests helps to offer the most appropriate products and ser-vices to them. This will increase customer-retention, growth and profitability of a business [5].

In this paper, we propose an intelligent LBS system, based on Bayesian networks and TOPSIS decision making method, to select the most appropriate advertisements for individuals. A similar work in this area is to produce personalized recom-mendations for tourist attractions, proposed by Huang et al. in [25]. The work published in [25] tries to recommend the most suitable tourist attractions to the users over the internet based on the Bayesian network technique and the Analytic Hierarchy Process (AHP) method. This system recommends tourist at-tractions to a user by considering the travel behavior users. In [26], the authors proposed a similar method for cellular phone selection.

In this paper, we propose an iLBS named Targeted Adap-tive Advertisement (TAAD) which is an advertising system that delivers the most appropriate advertisements to the users. Based on our knowledge, this is the first time that iLBS sys-tems are designed for advertisement system. In the proposed method, we first, obtain the user’s characteristics precisely, and then, we try to select the most suitable advertisements according to the user’s features. Bayesian networks are em-ployed to obtain user’s features and TOPSIS decision making method is used for ranking the advertisements.

The rest of the paper is organized as follows. The proposed system is presented in Section 2. In Section 3, it is illustrated that how Bayesian networks can be used to estimate the user’s characteristics. Section 4 describes Fuzzy TOPSIS technique for ranking the advertisements. In Section 5, we provide a case study to clarify the proposed method. Section 6 is dedicated to some concluding remarks.

II. TAAD ARCHITECTURE In the TAAD system, advertisement providers publish

their advertisements while LBS users consume it. Advertise-ment providers and users are autonomous components, ex-change information by publishing and subscribing to their field of interests. Users can subscribe in TAAD system, where they are being asked with some simple questions. Hence, sub-scribers’ characteristics can be determined for further use. On the other hand, providers send their advertisements into the TAAD. All of the provided advertisements are stored in adver-

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tisement queue for a predefined amount of time. All users that have previously expressed their characteristics will be notified by appropriate advertisements according to their current loca-tion and their inherent features. Figure 1 demonstrates an overall view of TAAD architecture. The first component ob-tains users’ features by utilizing Bayesian networks. This component has a great effect on the overall quality of the sys-tem. The central component of TAAD is ‘Decision Making’. which is concerned with ranking the available advertisements according to the users’ features. Finally, the ‘Preparing Adver-tisements’ component simply chooses the advertisements with the highest rank and sends out the advertisements to the users. The LBS middleware is a set of components which deals with communicating with the users properly.

III. ESTIMATING USER’S CHARACTERISTICS The main objective of the proposed method is to select and

recommend the advertising materials which best match with the user’s characteristics and features. Therefore, user’s fea-tures must be determined with a high consistency and reliabili-ty. However, naturally, these characteristics and features have a high level of uncertainty [6,7]. In order to overcome this issue we use Bayesian Networks (BN) in the proposed solu-tion. BN is a unifying framework to manage uncertainty in user’s preferences modeling. During the registration process, various psychological questions are posed to find out the us-er’s characteristics and features. While it is assumed that the mentioned questions are asked explicitly, another way is to find the answers in an implicit manner, by considering the behavior and activities of the user. In the following we explain how Bayesian Network works.

A. Bayesian Network concept Bayesian network (BN) is a probabilistic graphical model

which is used to represent casual relationship among variables [8]. In other words, BN is a combination of two definitions in mathematics [9]: probability theory and graph theory. BNs are models which capture uncertainties in terms of probabilities. If the Bayesian networks are correctly constructed they can be used to perform reasoning under uncertain conditions [10,8,11,12]. Directed acyclic graphs (DAG) are used to mod-el BNs. In this graph model, nodes represent propositional variables of interest. The directed arcs of a BN represent ca-sual dependencies among the variables [13,14]. For example an arc, on the graph, is a parent and child relation be-tween Y, as the parent, and X, as the child. While, the child node is dependent on its parent node, it is conditionally inde-pendent from others. These dependencies are quantified by conditional probabilities for each node given its parental nodes in the DAG. It allows us to compute the probability of a state of the variable given the state of its parent [9]. Figure 2 shows a simple Bayesian network. Bayesian network is based on the Bayes’ Theorem (1): | , |

(1)

where | is the conditional probability of X given Y. and are the probabilities of nodes X and Y respec-

tively.

Figure 1 - An overall view of TAAD architecture

Figure 2 - Demonstration of a simple Bayesian network

In real world, people utilize BN to compute the probability of each value of a node when the values of other variables are known. It is mainly used in processing indeterminate know-ledge. Applications of BN can be found in different areas such as action prediction, expert systems and data mining [15,2]. In the proposed method, simple Bayesian networks are employed to predict the behavior and characteristics of a mobile user based on predefined questions and the user’s behavior.

B. Using Bayesian Network for Estimating user’s features With the help of Bayesian networks, a user model is con-

structed to illustrate the relation between user’s characteristics and the predefined questions [27]. In using Bayesian network two issues is considered and the objective is twofold:

1) Obtaining the graph structure of the network and hence, the dependencies among the variables.

2) Obtaining the probability tables associated with each node.

In the proposed method, each user’s characteristic has its own BN. However, the structure of the BNs are similar. Each BN has a parent and a number of child-nodes. Parents are the user’s characteristics and children are the questions or user behaviors in our system. The probability tables were estimated by a psychological expert. As an example, in Figure 3 P S represents the presence probability of feature S in a certain user. These probabilities have been obtained from the statistics achieved by experts. Also, we can use these data to indicate that what will be the probability of a true answer to the ques-tion Q by a subscriber, if he/she has the feature S . In this Bayesian network P Q |S shows this probability (1 km . After evaluating the answers of the user to each relevant question, P S will be updated based on the following rules:

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Figure 3 - A sample BN

Figure 4- The membership function of the triangular fuzzy number

• If the answer is correct, will be increased.

• If the answer is incorrect, will be decreased.

For instance, in Figure 3, P S 0.6. If the user answers Q correctly, the probability of having this feature will be updated based on the following question: | |~~ 0.32 (2)

| | 0.75 (3)

It can be seen that P S is increased. However, what would be the result if the user selects the wrong answer to the question? By calculating the probability of P S |Q False , the result is 0.529. This process goes on for every question, related to this feature, in order to obtain the final estimation.

IV. RANKING ADVERTISING MATERIALS This section deals with ranking the preference order of ad-

vertisement materials. Fuzzy TOPSIS decision making method has been proposed in [16,17]. In the following subsections we show that how this method can be employed in the proposed solution.

A. Preliminary Definition of Fuzzy Data A triangular fuzzy number is a convex fuzzy set often

denoted by a triplet , , shown in Figure 4. Its mem-bership function defined as follows:

TABLE I. TRIANGULAR FUZZY NUMBERS FOR DEGREE OF IMPORTANCE AND ATTRIBUTE VALUE

Importance Fuzzy Number Attribute Value Fuzzy Number Very Minor (0, 0, 0.2) Very Low (0, 0, 2)

Minor (0, 0.2, 0.5) Low (0, 2, 5)

Medium (0.2, 0.5, 0.8) Medium (2, 5, 8)

Important (0.5, 0.8, 1.0) High (5, 8, 10)

Very Important (0.8, 1.0, 1.0) Very High (8, 10, 10)

Figure 5 - The membership function of attribute value

Figure 6 - The membership function for degree of importance

00

(4)

where is the most possible value of the fuzzy number , and and denote the lower and upper bounds of the fuzzy number, respectively.

The sum, product and the Euler distance between two tri-angular fuzzy numbers and can be defined as follows:

• Sum: , , (5)

• Product: , , (6)

• Euler distance: ,13 (7)

The judgment for the importance of decision objectives can be made in a way illustrated in Table I. The membership functions of the triangular fuzzy numbers for attribute value and degree of importance are shown in Figure 5 and Figure 6.

1

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B. Fuzzy TOPSIS Decision Making Concepts Decision making is the procedure of finding the best or

most suitable alternatives among a set of feasible alternatives. Decision making problems considering several criteria by multiple decision makers are called multi-criteria group deci-sion-making (MCGDM) problems [18,19,20].

A number of methods have been proposed in order to solve MCGDM problems. In this paper, TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) is em-ployed. TOPSIS is a well-known method for classical multi criteria decision making with numerous applications [21,22].

TOPSIS is based on the concept of a displaced ideal point which the final solution has the shortest distance [18]. Hwang and Yoon [21] proposed that the ranking between alternatives is based on the shortest distance from the positive ideal solu-tion (PIS) and the farthest from the negative ideal solution (NIS). This technique simultaneously considers the distances to both PIS and NIS and ranks the preference order according to their relative closeness and a combination of these two dis-tance measures. In this paper, we use an extended version of TOPSIS technique which is based on fuzzy data.

C. Using Fuzzy TOPSIS Decision Making Method Fuzzy TOPSIS method has three main parts: (1) Generat-

ing Fuzzy Decision matrix, (2) Obtaining weighted norma-lized decision matrix, (3) Calculating relative approach de-gree.

1) Fuzzy Decision Matrix Suppose that there are advertising materials (decision

attribute indexes) and user’s characteristics. If there are experts and the decision attribute value of the scheme given by the expert be 1 , 1 , 1

, then we can get the decision matrix given by the expert as follows:

(8)

Decision matrix values can be obtained based on the fol-lowing equation: 1 … (9)

Hence, the decision matrix will be as follows:

(10)

2) Weighted Normalized Decision Matrix

We should normalize the decision matrix . Each index can be normalized by the following equation:

TABLE II - MAPPING PROBABILITY OF USERS' FEATURE TO IMPORTANCE FUZZY NUMBER

Probability of a user’s feature Importance0 0.2 Very Minor0.2 0.4 Minor0.4 0.6 Medium0.6 0.8 Important0.8 1.0 Very Important

, , max (11)

Then, normalized decision matrix can be determined as follows: (12)

The Bayesian network defines the weight vector of attribute indexes , , , . This process is done through mapping the values calculated by Bayesian network to triangu-lar importance fuzzy numbers.

Finally by , we can obtain the weighted nor-malized decision matrix as follows:

(13)

3) Relative Approach Degree Now, we try to obtain the positive ideal point and the

negative ideal point for the normalized decision matrix in the following manner: , , , 1,1,1 , 1,1,1 , , 1, 1,1 (14)

, , , 0,0,0 , 0,0,0 , , 0, 0,0 (15)

Hence, the positive ideal point and the negative ideal point which are weighted and normalized can be obtained by the following equations: , , , , , , (16)

, , , , , , (17)

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The Euler distance from the scheme to the positive ideal point μ + can be calculated as follows:

, , (18)

, , (19)

(20)

Now, by considering the relative approach degree , we can rank the preference order of the advertising materials, greater values of results in higher rank for the related ma-terial.

V. A CASE STUDY ON MUSIC ADVERTISING This case study is a music advertising system based on the

proposed architecture. Suppose that there are six characteristics which are necessary to be examined before offering music Ad-vertisements to the users. These characteristics are self steam, creativeness, outgoing, at ease, introversive, and gentleness [23,24].

A mobile user, who has just subscribed in the system, will face with some questions in order to obtain his/her features. As an example three questions are asked to determine whether the user is self steam or not. We denote these questions by

, , and self stream feature by . Then, the corres-ponding Bayesian network will be constructed. Let 0.6. We define the probabilities as shown in Table III:

TABLE III - SELF STREAM CHARACTERSITIC PROBABILITY TABLE OF BN FOR A USER

Probability Value Probability Value Probability Value | 0.7 | 0.4 | 0.4 |~ 0.5 |~ 0.2 |~ 0.5 We assume that the user answers all of these questions

correctly. Therefore, can be calculated according to (2) and (3) as follows:

Step 1) For question 1: | |~~ 0.7 0.6 0.5 0.40.62 (21)

| | 0.677 (22)

Step 2) For question 2:

| |~~0.4 0.677 0.2 0.3230.3354

(23)

| |0.807 (24)

Step 3) For question 3: | |~~0.4 0.807 0.5 0.1930.4193

(25)

| |0.769 (26)

Hence, the user has the self steam characteristic with prob-ability of 0.769 and according to Table II it refers to ‘Impor-tant’ value. We repeat the above calculations for each question related to other characteristics to obtain the level of all user’s features.

In this case we have seven different tunes available for of-fering to the user and we want to select the most appropriate one. An expert defines the decision matrix as follows: 8,10,10 5,8,10 5,8,108,10,10 5,8,10 5,8,10 5,8,10 0,0,25,8,10 0,0,2 2,5,85,8,108,10,10 5,8,10 2,5,82,5,8 0,0,2 5,8,10 5,8,10 2,5,82,5,8 5,8,10 5,8,105,8,100,0,2 5,8,10 2,5,80,0,2 5,8,10 0,2,58,10,10 2,5,8 5,8,10 0,2,5 2,5,85,8,10 5,8,105,8,10 0,0,2 0,2,55,8,102,5,8

We can get the normalized decision matrix by formula (11) as follows: 0.8,1,1 0.5,0.8,1 0.5,0.8,10.8,1,1 0.5,0.8,1 0.5,0.8,1 0.5,0.8,1 0,0,0.20.5,0.8,1 0,0,0.2 0.2,0.5,0.80.5,0.8,10.8,1,1 0.5,0.8,1 0.2,0.5,0.80.2,0.5,0.8 0,0,0.2 0.5,0.8,1 0.5,0.8,1 0.2,0.5,0.80.2,0.5,0.8 0.5,0.8,1 0.5,0.8,10.5,0.8,10,0,0.2 0.5,0.8,1 0.2,0.5,0.80,0,0.2 0.5,0.8,1 0,0.2,0.50.8,1,1 0.2,0.5,0.8 0.5,0.8,1 0,0.2,0.5 0.2,0.5,0.80.5,0.8,1 0.5,0.8,10.5,0.8,1 0,0,0.2 0,0.2,0.50.5,0.8,10.2,0.5,0.8

The weight vector can be obtained by the results of Baye-sian networks as follows: 0.5,0.8,1 , 0.2,0.5,0.8 , 0.5,0.8,1 , 0,0.2,0.5 , 0.2,0.5,0.8 , 0.5,0.2, .0

Now we can get the weighted normalize matrix :

0.4,0.8,1 0.1,0.4,0.8 0.25,0.64,10.4,0.8,1 0.1,0.4,0.8 0.25,0.64,1 0,0.16,0.5 0,0,0.80,0.16,0.5 0,0,0.8 0.1,0.1,0.160.25,0.16,0.20.4,0.8,1 0.1,0.4,0.8 0.1,0.4,0.80.1,0.4,0.8 0,0,0.16 0.25,0.64,1 0,0.16,0.5 0.4,0.25,0.80,0.1,0.4 0.1,0.4,0.64 0.25,0.16,0.20.25,0.16,0.20,0,0.2 0.1,0.4,0.8 0.1,0.4,0.80,0,0.2 0.1,0.4,0.8 0,0.16,0.50.4,0.8,1 0.4,0.25,0.64 0.25,0.64,1 0,0.4,0.25 0.4,0.25,0.80,0.16,0.5 0.1,0.8,10,0.16,0.5 0,0,0.8 0,0.04,0.50.25,0.16,0.20.1,0.1,0.16

The distance of each music advertisement from the fuzzy positive idea solution and fuzzy negative ideal solution is de-termined as follows: , , , , , ,2.868, 2.874, 2.896, 2.896, 3.145, 3.218, 3.071

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, , , , , ,2.23, 2.191, 2.085, 1.815,2.142, 1.833, 2.759

And finally by calculating the relative approach degree, we can achieve the preference order: , , , , , ,0.437, 0.432, 0.418, 0.385, 0.405, 0.362, 0.473

It is almost obvious that :

.

Hence, the most appropriate Advertisement for the user is music number 7. If we are limited to send only three adver-tisements to an individual, with the above information, we can select numbers 7, 1, and 2. However, with the traditional ap-proach, we should select three advertisements at random, not necessarily satisfying the user.

VI. CONCLUSION In this paper, an intelligent targeted advertising solution

for mobile subscribers is presented. The presented method offers the most appropriate advertisements by considering the user’s individual characteristics. In the proposed method, Bayesian networks are used to recognize user’s features accu-rately. Also, Fuzzy data is employed in order to provide ap-proximation reasoning. In order to rank the available adver-tisements according to user’s features, TOPSIS technique is used. Combination of these components leads the approach to a high adaptive and personalized system in order to prevent from representing irrelevant advertising materials. While the efficiency of the proposed method is illustrated through an example, more detailed performance evaluation is required which will be performed in the future works.

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