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Improving Web Personalization via User Interest Hierarchy and Scoring Techniques

The World Wide Web is rampantly growing, providing users with a vast amount of information from which to search and explore. However, the retrieval of information is rather basic in that search engines often do not make distinctions between users; most search engines provide different users with identical results when their search queries are the same. We assert that this is naive and rather shallow, for the actual users are not identical, suggesting that their desired results are also not exactly the same. For example, if two users each search for “Windows,” maybe they desire two entirely different items: one user is interested in the aptly named operating system while the other is interested in purchasing glass panels through which to look. This scenario demonstrates the importance of learning what a particular user is interested in. Moreover, most current systems now only consider the specific term that a user searches for; however, it would be more appropriate if the system knew some underlying model of what the user is interested in. For example, say a user searches for “travel.” If the user has previously demonstrated interest in a particular scenic and distant country, it would be more useful to the user if his search results considered these past interests and considered that his traveling inquiries might concern going to that particular country. Additionally, a user’s interest may change over time. For this reason, we desire adaptively learning a user’s interests.