http://hdl.handle.net/1808/132 Search the Channel search http://kuscholarworks.ku.edu/dspace/simple-search http://hdl.handle.net/1808/5231 Title: Use of Radio Frequency Identification for Targeted Advertising: A Collaborative Filtering Approach Using Bayesian Networks<br/><br/>Authors: Cinicioglu, Esma Nur; Shenoy, Prakash P.; Kocabasoglu, Canan<br/><br/>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. Sun, 01 Jul 2007 00:00:00 GMT http://hdl.handle.net/1808/5230 Title: Decision Making with Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials<br/><br/>Authors: Cobb, Barry R.; Shenoy, Prakash P.<br/><br/>Abstract: Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for representing continuous chance variables in influence diagrams. Also, MTE potentials can be used to approximate utility functions. This paper introduces MTE influence diagrams, which can represent decision problems without restrictions on the relationships between continuous and discrete chance variables, without limitations on the distributions of continuous chance variables, and without limitations on the nature of the utility functions. In MTE influence diagrams, all probability distributions and the joint utility function (or its multiplicative factors) are represented by MTE potentials and decision nodes are assumed to have discrete state spaces. MTE influence diagrams are solved by variable elimination using a fusion algorithm. Tue, 01 Apr 2008 00:00:00 GMT http://hdl.handle.net/1808/5229 Title: Arc Reversals in Hybrid Bayesian Networks with Deterministic Variables<br/><br/>Authors: Cinicioglu, Esma Nur; Shenoy, Prakash P.<br/><br/>Abstract: This article discusses arc reversals in hybrid Bayesian networks with deterministic variables. Hybrid Bayesian networks contain a mix of discrete and continuous chance variables. In a Bayesian network representation, a continuous chance variable is said to be deterministic if its conditional distributions have zero variances. Arc reversals are used in making inferences in hybrid Bayesian networks and influence diagrams. We describe a framework consisting of potentials and some operations on potentials that allows us to describe arc reversals between all possible kinds of pairs of variables. We describe a new type of conditional distribution function, called partially deterministic, if some of the conditional distributions have zero variances and some have positive variances, and show how it can arise from arc reversals. Fri, 01 May 2009 00:00:00 GMT http://hdl.handle.net/1808/4250 Title: Order imbalance and stock returns: Evidence from China<br/><br/>Authors: Shenoy, Catherine<br/><br/>Abstract: We investigate the relation between daily order imbalance and return in the Chinese stock markets of Shenzhen and Shanghai. Prior studies have found that daily order imbalance is predictive of subsequent returns. On the Chinese exchanges we find autocorrelation in order imbalances is similar to that of the New York Stock Exchange as reported by Chordia and Subrahmanyam (2004). We also find a strong contemporaneous relation between daily order imbalances and return. However, we do not find evidence that order imbalances predict subsequent returns. We attribute the difference in predicative power to differences in trading mechanisms on the two exchanges and to differences in the share turnover rate. Mon, 01 Jan 2007 00:00:00 GMT