We propose a new webpage ranking algorithm which is personalized. Our idea is to rely on the attention time spent on a document by the user as the essential clue for producing the user-oriented webpage ranking. The prediction of the attention time of a new webpage is based on the attention time of other previously browsed pages by this user. To acquire the attention time of the latter webpages, we developed a browser plugin which is able to record the time a user spends reading a certain webpage and then automatically send that data to a server. Once the user attention time is acquired, we calibrate it to account for potential repetitive occurrences of the webpage before using it in the prediction process. After the user's attention times of a collection of documents are known, our algorithm can predict the user's attention time of a new document through document content similarity analysis, which is applied to both texts and images. We evaluate the webpage ranking results from our algorithm by comparing them with the ones produced by Google's Pagerank algorithm.