Abstract

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Personalized Internet Advertisement Recommendation Service Based on Keyword Similarity

Dipika Deshmukh, Dr. D.R. Ingle


— online advertising is major source of revenue for today’s online world. With rapid development of E-commerce audiences put higher requirements on personalized Internet advertisements.This study focus on developing a famework for personalized Internet advertisement recommendation service. Personalized advertisement aims to most suitable advertisement for anonymous users on website. All advertisements are categorized by commercial categories provided by yahoo. Then keywords are extracted from each advertisement and the most correlated keywords to each category are identified through Term Frequency and Inverted Domain Frequency (TF-IDF) analysis. Thus, the ontology of the advertisement is built. Normalized Google Distance (NGD) relationships between keywords are computed to derive the characteristic vector of each advertisement. Besides, based on user’s responses to some advertisements, the user profile, which describes a user’s preferences for advertisements, is established through logistic regression. Finally, for a new advertisement, a recommendation value is computed by using the characteristic vector and the user profile. The value is used to determine whether this advertisement should be recommended to the user or not. A prototype website for verifying the proposed schemes was developed.