Personalized Information Retrieval by Using Adaptive User Profiling and Collaborative Filtering

Abstract

Many search engines such as Yahoo, Google, MSN, and AltaVista, have been developed to meet various users’ search needs in real world. In general, because of the lack of the personal information such as hobby, preferences, and interests, these existing information retrieval systems are unsuitable to provide personalized search results to users. In this paper, we propose an adaptive user profiling method using dynamic updating policy considering the change of the users’ preferences over time and domain. Moreover, we employ collaborative filtering method to handle the situation that users’ preferences are frequently or continuously changed. Experimental results show that our method considerably improved personalized search performance for each user through automatic creation, maintenance, and personalization of user preference profiles that include search patterns of individual users.