Implementation of a personalized information system on mobile phones Pablo López Escobés Advanced Services Enabling Telematic Applications MSc ICT Research University of Valladolid January 7, 2010

Introduction Today, users receive lots of publicity from many places This huge amount of publicity means that in many cases ineffective and is automatically discarded Personalized advertising is a good option for companies and users because: ▫The amount of publicity received by users is smaller ▫Businesses cut costs because they send less advertising ▫Advertising has a better user acceptance

A new way of advertising In order to send personalized advertising is necessary: ▫That users have a personal element that allows them to receive advertising ▫Companies are aware of user preferences to determine if they are interested in sending advertising or not ▫Create a system where companies can meet these data and through which they can send personalized advertising to users

Determining a specific group of users Users must carry an instrument to receive advertising To achieve good system performance, this instrument should be as personal as possible The instrument has to be non-intrusive and with daily use There is an object that meets the above requirements THE CELL PHONE

Determining a specific group of users System should be able to locate cell phones in an indoor or outdoor location accurately GPS (Global Position System) is a good option for outdoor locations, but fails indoor locations Infra-Red or Wi-Fi implementations are not useful because requires new infrastructures or accessories to cell phones A GSM algorithm is the best option ▫includes the 6-strongest GSM cells and readings of up to 29 additional GSM channels ▫Leverages the phone’s existing hardware and removes the need for additional radio interfaces

Obtaining the users profiles Once is known the specific group of users, it's time to get their different profiles There are several algorithms which try to obtain different user profiles based on statistical study and data mining The approach selected to model the users is a simple but effective system in which users rate some items and algorithm later is able to predict the users true rating about other items. ▫Given a particular item and user, the goal is to predict the user's true rating for the item in question. ▫System generates an statistical profile based in general items like genre or age and custom items, provided by the companies interested in send advertisements to users

How to decide who can send the information? Companies want to send their advertising to specific users only Companies must report which user parameters want to know System obtain profiles based on all parameters passed by companies It is possible that several companies want to advertise to the same users

How to decide who can send the information? The goal is a real-time application, in which each company would be able to know who is in his action radio Each company would be able to compete with other companies to send their advertising to a user group It was decided that the system used to determine which advertisements are sent to users will be based on auctions ▫users are benefiting because the information that receives them is focused on them ▫Competition among companies to advertise, will increase the revenue of the company which provides this service

The auction system The application will be web based because companies should be able to connect with the system in real time to bid The interface will be intuitive and should provide in an easy and simple way all the statistical data Companies must have all available information to make their bids knowing all the possibilities.

The auction system Time to time, depending on each situation, the system looks for users located within the area and generate statistical study with different user profiles System will notify via to all the companies registered in the system that a new auction is available. Companies review statistical study results and in light of that, consider whether it is interest then send their advertising to users located in the area By notifications via companies follow the course of the auction

The auction system At the end of the auction process a notification is sent to the winning company that can send his advertising At that time it displays a form to the winning company in which introducing the parameters of the message to send to users Users receive personalized advertising messages in their own cell phone

System schema Detection of users Companies are subscribed to auction system Companies indicate their own elements of user characterization Modelling users Companies bid for advertisements Users receive personalized advertising messages in their own cell phone

References [1] Dey, A. and Abowd, G., ―Towards a Better Understanding of Context and Context-Awareness ‖, Workshop on the what, who, where, when and how of context-awareness at CHI 2000, April 2000 [2] Varshavsky, A, GSM indoor localization. Pervasive and mobile computing. Volume 3, Issue 6 [3] Marlin, B, ―Modeling User Rating Profiles for Collaborative Filtering ‖, Proc. 17th Ann. Conf. Neural Information Processing Systems (NIPS ’03), 2003 [4] P. Bahl, V.N. Padmanabhan, RADAR: An in-building RF-based user location and tracking system, in:Proceedings of INFOCOM, [5] L. Aalto, N. Gothlin, J. Korhonen, T. Ojala, Bluetooth and WAP push based location-aware mobile advertising system, in: Proceedings of the 2nd International Conference on Mobile Systems, Applications, and Services, ACM Press, [6] N.B. Priyantha, A. Chakraborty, H. Balakrishnan, The cricket location-support system, in: Proceedings of the Sixth Annual ACM International Conference on Mobile Computing and Networking, [7] A. Ward, A. Jones, A. Hopper, A new location technique for the active office, IEEE Personal Communications 4 (5) (1997). [8] Lionel M., LANDMARC: Indoor Location Sensing Using Active RFID, Wireless Network, Volume 10, Number 6 [9] Zukerman, I., & Albrecht, D.W. (2001). Predictive statistical models for user modeling. User Modeling and User-Adapted Interaction, 11, [10] Amobee, A plattform to ad-funding the mobile business. Available at: Last visit December 30, 2009 [11] Broder, A., M. Fontoura, V. Josifovski and L. Riedel, ―A Semantic Approach to Contextual Advertising, ‖ Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, ACM Press, [12] M. Ciaramita, V. Murdock, and V. Plachouras. Semantic associations for contextual advertising. International Journal of Electronic Commerce Research—Special Issue on Online Advertising and Sponsored Search, 2008.

Thanks for your attention