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Personalized Ranking Of Search Results With Implicitly Learned User Interest Hierarchies, Hyoung-Rae Kim, Philip K. Chan
Personalized Ranking Of Search Results With Implicitly Learned User Interest Hierarchies, Hyoung-Rae Kim, Philip K. Chan
Electrical Engineering and Computer Science Faculty Publications
Web search engines are usually designed to serve all users, without considering the interests of individual users. Personalized web search incorporates an individual user's interests when deciding relevant results to return. We propose to learn a user profile, called a user interest hierarchy (UIH), from web pages that are of interest to the user. The user's interest in web pages will be determined implicitly, without directly asking the user. Using the implicitly learned UIH, we study methods that rank the results from a search engine. Experimental results indicate that our personalized ranking methods, when used with a popular search engine, …
Learning Implicit User Interest Hierarchy For Context In Personalization, Philip K. Chan, Hyoung-Rae Kim
Learning Implicit User Interest Hierarchy For Context In Personalization, Philip K. Chan, Hyoung-Rae Kim
Electrical Engineering and Computer Science Faculty Publications
To provide a more robust context for personalization, we desire to extract a continuum of general (long-term) to specific (short-term) interests of a user. Our proposed approach is to learn a user interest hierarchy (UIH) from a set of web pages visited by a user. We devise a divisive hierarchical clustering (DHC) algorithm to group words (topics) into a hierarchy where more general interests are represented by a larger set of words. Each web page can then be assigned to nodes in the hierarchy for further processing in learning and predicting interests. This approach is analogous to building a subject …