With the growing amount of information available on the Web, the task of retrieving documents of interest has become increasingly difficult. Personalized search has got significant attention because it considers the user's preference into the search process to improve the results. However, studies have shown that users are reluctant to provide any explicit input on their personal preference. In this study, we investigate how a search engine can elicit users' preferences by exploring the user's tagging activity from various sources. We propose a simple yet flexible model to succinctly represent user preferences based on multiple factors. Our experiments show that users' preferences can be elicited from a multi-factor tagging data and personalized search based on user preference yields significant precision improvements over the existing ranking mechanisms in the literature.