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Use of Radio Frequency Identification for Targeted Advertising: A Collaborative Filtering Approach Using Bayesian Networks
Cinicioglu, Esma N. Shenoy, Prakash P. Kocabasoglu, Canan
Cinicioglu, Esma N.
Shenoy, Prakash P.
Kocabasoglu, Canan
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Abstract
This article discusses a potential application of radio frequency identification (RFID) and collaborative filtering for targeted advertising in grocery stores. Every day hundreds of items in grocery stores are marked down for promotional purposes. Whether these promotions are effective or not depends primarily on whether the customers are aware of them or not, and secondarily whether the customers are interested in the products or not. Currently, the companies are incapable of influencing the customers’ decision-making process while they are shopping. However, the capabilities of RFID technology enable us to transfer the recommendation systems of e-commerce to grocery stores. In our model, using RFID technology, we get real time information about the products placed in the cart during the shopping process. Based on that information we inform the customer about those promotions in which the customer is likely to be interested in. The selection of the product advertised is a dynamic decision making process since it is based on the information of the products placed inside the cart while customer is shopping. Collaborative filtering will be used for the identification of the advertised product and Bayesian networks will be used for the application of collaborative filtering. We are assuming a scenario where all products have RFID tags, and grocery carts are equipped with RFID readers and screens that would display the relevant promotions.
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Date
2007-07
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Springer-Verlag, Berlin
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Keywords
Rfid, Targeted advertising, Bayesian networks, Learning Bayesian networks, Collaborative filtering
Citation
Cinicioglu, E. N., P. P. Shenoy and C. Kocabasoglu, "Use of Radio Frequency Identification for Targeted Advertising: A Collaborative Filtering Approach Using Bayesian Networks," in K. Mellouli (ed.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Lecture Notes in Artificial Intelligence, Vol. 4724, 2007, pp. 889--900, Springer-Verlag, Berlin