Advertisement Mining using Hidden Markov Model

Predicting the individual's web-browsing behavior on the internet is more important for advertisement mining. This makes the advertisers or the publishers to successfully interact with users in providing relevant advertisement, many intelligent interfaces requires a method for recognizing, analyzing and predicting user behavior actions. Initially we need to collect a log of datasets with user behavior attributes. Hidden Markov Model is used to derive a pattern for predicting the behavior of the users on the web. Also, it helps in analyzing the performance of user behavior profile and estimating the advertising cost to perform Mapreduce jobs based on user's search query. With these collected information, optimized advertisement is deployed to the user using Bigquery mechanism.