Effective profiling of consumer information retrieval needs: a unified framework and empirical comparison

Due to the overwhelming volume of information that is increasingly available, many people rely on current awareness systems to keep abreast of the latest developments in the fields that they are interested in, as evidenced in the popularity of subscriptions to news-monitoring and digital library services. The success of these services, however, often requires effective acquisition of users' personal standing interests as represented in personal profiles. Our objective in this paper is twofold. First, we have introduced a new method for profile generation and compared it against other well-known methods. We have found promising results. Second, although there are various methods proposed in information retrieval and machine learning literature to address the issue of profiling, a unified framework and systematic cross-system comparison to help users, especially service providers, to determine the most effective way of profiling consumers is still lacking in the literature. In this paper, we try to fill the gap by looking at these methods from a more integrated point of view based on statistical contingency theory. Variations of these methods are then systematically tested on three well-known routing systems and results are analyzed and reported.