Object Oriented Implementation of Concept-Based User Profiles From Search Engine Logs
User profiling is a fundamental component of any personalization applications. Most existing user profiling strategies are based on objects that users are interested in (i.e., positive references), but not the objects that users dislike (i.e., negative preferences). In this paper, the authors focus on search engine personalization and develop several concept-based user profiling methods that are based on both positive and negative preferences. They evaluate the proposed methods against their previously proposed personalized query clustering method. Experimental results show that profiles which capture and utilize both of the user's positive and negative preferences perform the best. An important result from the experiments is that profiles with negative preferences can increase the separation between similar and dissimilar queries.