Categorizing information objects from user access patterns

Many web sites have dynamic information objects whose topics change over time. Classifying these objects automatically and promptly is a challenging and important problem for site masters. Traditional content-based and link structure based classification techniques have intrinsic limitations for this task. This paper proposes a framework to classify an object into an existing category structure by analyzing the users' traversals in the category structure. The key idea is to infer an object's topic from the predicted preferences of users when they access the object. We compare two approaches using this idea. One analyzes collective user behavior and the other each user's accesses. We present experimental results on actual data that demonstrate a much higher prediction accuracy and applicability with the latter approach. We also analyze the correlation between classification quality and various factors such as the number of users accessing the object. To our knowledge, this work is the first effort in combining object classification with user access prediction.