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.

In a categorized information space, predicting users ’ information needs at the category level can facilitate personalization, caching and other topic-oriented services. This paper presents a two-phase model to predict the category of a user’s next access based on previous accesses. Phase 1 generates a snapshot of a user’s preferences among categories based on a temporal and frequency analysis of the user’s access history. Phase 2 uses the computed preferences to make predictions at different category granularities. Several alternatives for each phase are evaluated, using the rating behaviors of on-line raters as the form of access considered. The results show that a method based on re-access pattern and frequency analysis of a user’s whole history has the best prediction quality, even over a path-based method (Markov model) that uses the combined history of all users.

Ratings for products and services are increasingly important on the Internet, as they allow users to harvest the wisdom of the community in making decisions. However, the difficulty with ratings is that little is known about the people providing them. Interpreting ratings well requires that the reputations of raters be factored into the scores computed for rated objects, even though these reputations are not explicitly available. Taking advantage of the insight that reputation can be computed implicitly from ratings, this paper addresses the reputation problem for raters and its application to evaluating rated objects. We develop a general method to automatically compute reputations for raters based on the ratings they and others give to objects, and incorporate these reputations to generate value-added information about rated objects. We evaluate our mechanisms by performing experiments on data from major rating sites, and show that they have the desired properties of a good reputation system. In the process, we analyze some key characteristics of different types of Internet ratings. To our knowledge, this is the first investigation into automatically computing raters'reputations and applying these reputations to better evaluate rated objects.