Personalized Scheduling Search Advertisement by Mining the History Behaviours of Users

In this paper, we tackle the problem as how to personalize the scheduling of ads for different users with objective to maximize the revenue of the search engine. In other word, our object is to deliver ads to individual users based on their interests inferred from user´s history queries and history clicked documents. For each query and document we use five important features including the unigrams, categories, phrases, brands, loss memory of an ad. These features are extracted from each ad relevant to current query. Our simulation experiments show that our proposed scheduling algorithm is promising in increase of the revenue of search engine and the click through rate of each user.