Modeling and Predicting User Interests based on Taxonomy

In the thesis, we analyze user interests based on a domain specific taxonomy. We propose modeling user interests and measuring similarity of users according to the taxonomy in the domain. Then we apply our method to recommender systems. We propose identifying topics, those that include new concepts that are likely be interesting to the user even though those concepts are not present in the user profile. We try to expand user interests significantly by letting the user browse those topics. Recommender systems are widely used by content providers to drive their commercial success. Many content providers adopt methods based on collaborative filtering (CF), which is a broad term for the process of recommending items to an active user, who receives the recommendation, based on the intuition that users who access the same items with the user tend to have similar interests with the user. Basic CF methods measure the similarity of users only from the co-rating behaviors against items, and compute recommendation for the active user by analyzing the items possessed by the most similar users with the user. As a result, they are apt to recommend the types of items that have already been accessed by the user. For example, if the user highly rates a horror movie (as an item), the typical CF methods recommend items that were made by the same director, performed by the same actors, or included in the same genre, horror. Those items are not truly novel since they are often already known to the user, or easily discovered by the user. We also apply our method to knowledge management in a system devel-