Ranking user's relevance to a topic through link analysis on web logs

Computing the web-user's relevance to a give topic is an important task for any personalization service on the Web. Since the interest and preference of a web-user are revealed in his Web browsing history, in this paper we develop a novel approach that utilizes Web logs to compute the relevance of a web-user to a given query. In contrast to traditional methods that are purely based on textual analysis, our approach calculates the web-user's relevance through link analysis under a unified framework where the importance of web-pages and web-users mutually reinforce each other in an iterative way. The experimental results show that our approach has achieved 53 of accuracy when ranking the web-user's relevance to a search topic.