We propose a new method for personalized webpage recommendation. The method is capable of inferring a user's personal reading interest distribution according to implicit user feedbacks coming from the user's past online reading activities. With the inferred user reading interest distribution, we can recommend webpages in a search result set to a user in a personalized way. Our method is featured by its novel approach to observe the facial expressions and gaze positions of a user during the user's online reading activities as two types of implicit user feedbacks for estimating the user's reading interest distribution. To capture these implicit user feedbacks, we use an ordinary web camera and a customized web browser in the setup. The setup allows us to measure the distribution of the reading time a user spends in his or her reading activities over materials of different contents. With all the captured information, our method then estimates a user's reading interest distribution by finding correlations between the implicit feedbacks of a user with the contents of the read materials. Given the estimated user reading interest distribution, our algorithm can further predict the user's potential reading interest in any new webpage. Consequently, our algorithm can produce a personalized webpage recommendation for all the result webpages in an online search session. We compared the performance of our method with that of several mainstream commercial search engines as well as a recent personalized webpage ranking algorithm. The comparison results clearly show the superiority of our new method for personalized webpage recommendation.