An effective general framework for localized content optimization

Local search services have been gaining interests from Web users who seek the information near certain geographical locations. Particularly, those users usually want to find interesting information about what is happening nearby. In this poster, we introduce the localized content optimization problem to provide Web users with authoritative, attractive and fresh information that are really interesting to people around the certain location. To address this problem, we propose a general learning framework and develop a variety of features. Our evaluations based on the data set from a commercial localized Web service demonstrate that our framework is highly effective at providing contents that are more relevant to users' localized information need.