Recommending Web Advertisements based on Long-Short Term User Interest

This paper reports the results of a study carried out to develop a system to recommend web advertisements to users based on their latent interests in an online real time bidding environment. As part of this work, we describe an approach which could be used to help predict the latent interest of users by analyzing their long and short term interests based on a large dataset of user web browsing histories. The proposed approach was tested in an experiment study with 32 different websites. Overall, this approach, which separated the user browsing history into sections representing their long and short term interests resulted in significantly higher predictive performance than when a singular section of user browsing history was used to represent the overall interests of users. In addition, we examined the effect of using different category levels as features to represent long and short term interest.