The purpose of this research is to develop a web ad selection model for one-to-one advertisement using neurofuzzy systems. The objectives of the paper are i) to present a one-to-one web advertising model to develop a "learning relationship" with a customer, ii) to suggest the fuzzy inference approach for web ad selection instead of the classical mathematical programming approach to determine a precise match of a specific ad type for a specific customer, and iii) to describe how to select web advertisements based on customers’ profile data using a sample scenario. The paper develops a method to identify web ads on a web site based on the customer's general behavior and demographic information. This targeting is based on preferences and quantifiable demographic data. A key aspect of the approach in this paper is that it uses the neuro-fuzzy (combining neural network and fuzzy systems) perspective borrowed from the engineering literature in soft computing.
Kim, Dan Jong; Ramesh, M.; and Rao, H. R., "Web Ads Selection for One-to-One Advertising Using Neuro-Fuzzy Systems" (2000). AMCIS 2000 Proceedings. 389.