MADVERTISER: A SYSTEM FOR MOBILE ADVERTISING IN MOBILE PEER-TO-PEER ENVIRONMENTS

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1 Association for Information Systems AIS Electronic Library (AISeL) PACIS 2014 Proceedings Pacific Asia Conference on Information Systems (PACIS) 2014 MADVERTISER: A SYSTEM FOR MOBILE ADVERTISING IN MOBILE PEER-TO-PEER ENVIRONMENTS Wan-Shiou Yang National Changhua University of Education, wsyang@cc.ncue.edu.tw Bing-Ruei Lin National Changhua University of Education, brlin@cc.ncue.edu.tw Follow this and additional works at: Recommended Citation Yang, Wan-Shiou and Lin, Bing-Ruei, "MADVERTISER: A SYSTEM FOR MOBILE ADVERTISING IN MOBILE PEER-TO-PEER ENVIRONMENTS" (2014). PACIS 2014 Proceedings This material is brought to you by the Pacific Asia Conference on Information Systems (PACIS) at AIS Electronic Library (AISeL). It has been accepted for inclusion in PACIS 2014 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact elibrary@aisnet.org.

2 MADVERTISER: A SYSTEM FOR MOBILE ADVERTISING IN MOBILE PEER-TO-PEER ENVIRONMENTS Wan-Shiou Yang, Department of Information Management, National Changhua University of Education, Changhua, Taiwan, R.O.C., wsyang@cc.ncue.edu.tw Bing-Ruei Lin, Department of Information Management, National Changhua University of Education, Changhua, Taiwan, R.O.C., brlin@cc.ncue.edu.tw Abstract Mobile advertising provides an opportunity to connect with consumers on a personal level, with ads that reflect their on-the-go need states and moments of decision. Mobile advertising systems thus have attracted much attention in both research and practice in recent years. In this research, we designed a novel system to provide peer-to-peer mobile ads. By using the designed system, a point of interest can send out an ad to a mobile user who is close by. When two mobile users are within a certain distance, their ads may be exchanged. In this research, we therefore propose approaches for mobile peers to exchange their ads and develop an incentive model for encouraging people share their data. We preliminarily evaluate the performance of the designed system by using simulation approach. Through simulated experiments, it was shown that the proposed system can distribute ads effectively but avoid the over flooding problem. Keywords: Mobile Advertising, Mobile Peer-to-Peer, M-Commerce

3 1. INTRODUCTION With the advent of techniques, mobile phone has been becoming a part and parcel of lives of most people of these days. Mobile advertising thus provides a unique opportunity to connect with consumers on a personal level, with ads that reflect their on-the-go need states and moments of decision. In a new Gartner report (Baghdassarian and McGuire, 2013), mobile advertising is forecast to be the most important driver of the global advertising economy over the next three years. Mobile advertising systems therefore have attracted much attention in both research and practice in recent years (Kourouthanassis and Glaglis, 2012). With a fervent interest in the development of mobile advertising services, we are engaged in an intelligent mobile advertising (IMA) project. The project, whose principal goal is to develop technologies for supporting mobile advertising services, is a marriage of three series investigations. The first investigation involved the design and construction of a mobile advertising system, termed as the MAdvertiser system. The second investigation addresses the media representation issues of mobile ads, and the third investigation focuses on the integration of diverse advertising sources. The preliminary progress of the first investigation is reported in this paper. The first investigation, the MAdvertiser system, aims at serving interest-based ads dissemination in mobile environment. The core of the MAdvertiser system is an ad exchanging model, which exchanges ads by taking user s preferences into account. Also, it adopts a mobile peer-to-peer framework so that a mobile device can continually detect nearby devices and directly exchange information with them without additional infrastructures. Points of interests (POIs) therefore can use the MAdvertiser system to broadcast ads to nearby interested users. Some preliminary experimental results are also shown in this paper. This paper is structured as follows. In Section 2, we review related efforts in this context. In Section 3, we describe our approaches for designing the MAdvertiser system. In Section 4, we present some preliminary evaluation results. In Section 5, we summarize this work and point out our future research directions. 2. LITERATURE REVIEW Mobile advertising is a new approach in advertisement strategy nowadays. In basic terms, mobile advertising is the process of planning and execution conception, pricing promotion and distribution of products and services through the mobile channel (MMA, 2003). A few studies have been conducted to show the effectiveness of mobile advertising as well as

4 categorizing the system (Barwise and Strong, 2002; Park et al., 2008; Hopkins and Turner, 2012). Basically, there are two possible ways to deliver the advertisements to end user s mobile devices: (1) Push-based: The advertisements are sent to the recipient s mobile device without their request, (2) Pull-based: The recipient requests the advertisements from a server. Numerous systems had been invented using both the implementations (Park et al., 2008; Hopkins and Turner, 2012). In this research, we adopt the push-based concept to design the proposed MAdvertiser System. Technologies of mobile devices and RF-based communications (e.g., Bluetooth or Wi-Fi) have made it possible to form peer-to-peer connections in mobile settings (Wolfson et al., 2005). In a mobile peer-to-peer environment, a peer is capable of detecting the peers that are within its transmission range. If two peers are geographically close for a period of time, certain information may be exchanged. Mobile peer-to-peer networking is an attractive choice for designing various kinds of applications due to its ease of deployment and no need for additional infrastructure (Wolfson et al., 2005; Gottron et al., 2010). Considering cost and popularity, we adopt Bluetooth in the current version of the MAdivertiser system. In this research, we also develop an incentive mechanism to stimulate cooperation in the process of data dissemination. Various incentive mechanisms based upon virtual currency have been investigated (Mondal, et al., 2007), and can be classified into two types: (1) Producer-paid: The producer of an item pays an advertisement fee by attaching a certain amount of virtual currency in the announcement item. Each mobile node that transmits the item withdraws a commission fee from the advertisement fee. (2) Consumer-paid: Rather than the producer, the consumer of an item pays the fee. The amount of the fee usually depends on the relevance of the item at the time of trading. In this research, we adopt the producer-paid model to design our incentive mechanism. 3. MADVERTISER SYSTEM In this research, we intend to provide mobile advertising service so that users can receive ads in mobile environments. We propose a distributed architecture, shown in Figure 1, in which each user is equipped with a small device implementing the user-side system and a server is located in each POI, implementing the POI-side system. By using the proposed system, a POI may announce an ad and select a nearby user to send out the ad. When two users meet, they may exchange ads stored in their local database so that ads can be disseminated. A unique feature of our approach is to consider user s preference in the process of data dissemination. In the following, we describe the design of each component of the proposed architecture.

5 User-side system Other user POI-side system Location manager Local storage User data manager POI data manager Recommendation manager POI database User Figure 1. The overall architecture of the MAdivertiser System A user-side system in the architecture comprises three components: the Location manager, the Recommendation manager and the User data manager. In the proposed system, POIs are grouped into several types, and a user is initially required to give her rating, in the range of 1 and 10, on each POI type. This rating data shows the user s preference and is stored in user s mobile device via User data manager. Formally, suppose there are totally h POI types. A user s ratings on POI types, called a rating list, can be regarded as a h-tuple <r 1,r 2, r i,,r h >, where r i is the user s rating on the i th POI type, in a scale of 1 to 10. When a user visits a POI, the user s rating list will be collected by POI data manager and stored in the POI s database. Therefore, POI s database stores the rating lists it received from users who have visited the POI. When a POI comes to making advertisement, the POI s data manager firstly selects a set of rating lists in its database. This set of rating lists represents the preference of the announced ad s target user. The POI may select the set of rating lists according to its marketing strategies, such as recency, frequency, monetary analysis (Bult and Wansbeek, 1995). Specifically, given a set S of rating lists, the preference of an ad s target user is the average of S. Therefore, an ad announced by a POI can be regarded as 3-tuple <C, R, B>, where C is the message content of the ad, R is the preference of its target user, and B is the budget of the ad (described later). To prevent ads from over-circulating (and thus causing information flooding problem), we adopt an interest-based data dissemination approach in this research. That is, when a POI announces an ad, its data manager selects the first user who is similar to the ad s target user and sends out the ad. Specifically, when user i is within the proximity distance of a POI j and the connection is established, j collects the rating list of user i. Suppose j now announces an ad = <C, R, B>, the ad will be send out to user i if the similarity between the preference R and the rating list of user i are higher than a threshold Th. If not, the POI will wait for next user.

6 The similarity between two rating lists is computed using Pearson correlation coefficient. Also, the interest-based data dissemination approach is used when two users meet. When two users are close to each other, they first exchange their own rating lists. Based on received rating list, the device retrieves the similar ads from its database and sends out. Specifically, when user i is within the proximity distance of another user j and the connection is established, it sends out its own rating list and receives the rating list of user j. The ads in i s database whose similarities to the rating list of user j are higher than a threshold Th are retrieved and sent out. The success of data dissemination in the proposed system heavily relies on cooperation among mobile users. This raises the issue of encouraging people share their data so as to maximize the revenue of the whole group. In the proposed system, we therefore propose to utilize economic incentives for the efficient processing of data dissemination. At a high level, our model works as follows. To prevent an ad from over-circulating, an ad only incurs less than K propagations, where K is a POI (i.e., producer) specified constant. Also, a user earns a flat commission fee f each time it transmits an ad to another user. Therefore, when an ad is announced by a POI, the POI loads with B a number of coins, B = K * f, called the initial budget. When a user transmits an ad to another user, the user earns commission fee f, and the remaining propagation number of the ad is divided between the sender and receiver in order for both to keep propagating the ad. Specifically, let X be a user that carries an ad D. Let K be the current propagation number of D. Suppose that X encounters Y. If both X and Y have D, then X and Y will not update their coin counters. Otherwise, X increases its coin counter by f, and sets the budget of D to be!!!!!!. Y sets the budget of its copy of D to be. If the!! remaining propagation number is 0, the ad will not be transmitted in the future. Once a user s local storage contains sufficient amount of ads, the Recommendation manager considers both the user s preference and the current position information given by the Location manager for recommending ads. Specifically, the similarities between ads and the rating list of the user are rounded, e.g., 9.15 becomes 9. Ads are then sorted in descending order of their (rounded) similarities, and those that have the same scores are listed in ascending order of their distances to the current position of user i. Finally the first N ads in the list, where N is given by the user, are recommended. 4. PRELIMINARY EVALUATION In this section, we preliminarily report some evaluation results of the proposed system

7 conducted by simulation approach. 4.1 Simulation model The simulation scenario is about visiting POIs in a theme park. In our simulation model, there are N U users and N A POIs. POIs are randomly located in an L L area and represented by their centers. The distance between two POI P 1 (x 1, y 1 ) and P 2 (x 2, y 2 ) is measured by their Manhattan distance, which is x 1 - x 2 + y 1 - y 2. A POI periodically announces an ad by following exponential distribution with parameter λ. A user i moves from a POI to another at a fixed speed S i,which is randomly determined by following uniform distribution: U(S min, S max ). Users do not exchange data while they are moving. When a user i arrives at a POI, she will stay there for a certain time Duration i ~ U(0, Duration max ). For each user j who is also in the POI, user i has the probability P i to exchange ads with user j, where P i is proportional to Duration i. The preference of each user is represented as a rating vector with dimensions being POI types. In this simulation model, users and POIs are grouped into N UG user groups and N AT POI types respectively. Users in the same user group have the same interests on POI types. In the experiments, each user group is assumed to express interests on two POI types. Ratings of users on POIs are integer numbers ranging from 1 to 10, with 1 being least interest and 10 being highest interest. The ratings of interested POI types are in [8, 10], whereas [1, 7] is the range for non-interested POI types. In the simulation process, a user randomly chooses a POI from her preferred POI types to visit. Each user leaves the simulation after she visited M POIs. When all users leave the system, the simulation ends. Table 1 lists all the parameters and their settings used in the simulation model. Parameter Meaning Setting N U The number of users 1000 N A The number of POIs 200 N UG The number of user groups 10 N AT The number of POI types 10 SL The side length of the simulated square area 10KM S max Maximum speed of a user 670M / min S min Minimum speed of a user 160M / min S i The speed of user i U(S min, S max ) Duration max The maximum amount of 60 time a user will stay in a POI (unit: minute) Duration i The amount of time a user i stays in a POI U(0, Duration max ) M Maximum number of POIs a 20 user visits Th Similarity Threshold 0.7 Table 1. Parameters used in the simulation 4.2 Experimental Results

8 First, we examine how an ad is spatially distributed. Figure 2(a) shows the average density histogram. From Figure 2(a) it can be seen that the copies converge to the home. It is observed that there is a boundary radius (in this case about 700 meters) such that there is no copy outside the area defined by the boundary radius. This indicates that the proposed system automatically obtains a balance between the availability of ad and the cost of exchanging and storing them. Copies per square 100m (a)the average density histogram Distance to home (100m) Similarity (b)the average similarities Our approach The Random approach Different approach Figure 2. Experimental results: (a) The average density histogram (b) The average similarities Then, we examine who receives the distributed ads. We compare the performance of the proposed approach with that of the Random approach. For the Random approach, a POI randomly select a nearby user to send out its ad, and 20 ads are randomly selects to exchange when two users meet. The averaged similarities of the preferences of ads and the rating lists of all received users are reported in Figure 2(b). From Figure 2(b) it can be seen that the proposed approach outperform the Random approach. This indicates that more ads will be distributed to users who are interested about them. 5. CONCLUSIONS In this research, we design a novel system to provide mobile advertising service in a mobile peer-to-peer environment. Through preliminary simulated experiments, it was shown that the proposed system can distribute ads effectively but avoid the over flooding problem. Our work will be extended in several directions. First, it is important to design mechanism to determine suitable parameters. Second, it is essential to test the system in a real-world environment. Finally, it is interesting to integrate some security techniques so that users can adopt the advertising service safely.

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