Learning for automatic personalization in a semantic taxonomy-based meta-search agent

摘要

Providing highly relevant page hits to the user is a major concern in Web search. To accomplish this goal, the user must be allowed to express his intent precisely. Secondly, page hit rating mechanisms should be used that take the user’s intent into account. Finally, a learning mechanism is needed that captures a user’s preferences in his Web search, even when those preferences are changing dynamically. To address the first two issues, we propose a semantic taxonomy-based meta-search agent approach that incorporates the user’s taxonomic search intent. It also addresses relevancy improvement issues of the resulting page hits by using user’s search intent and preference-based rating. To provide a learning mechanism, we first propose a connectionist model-based user profile representation approach, which can leverage all of the features of the semantic taxonomy-based information retrieval approach. A user profile learning algorithm is also devised for our proposed user profile representation framework by significantly modifying and extending a typical neural network learning algorithm. Finally, the entire methodology including this learning mechanism is implemented in an agent-based system, WebSifter II. Empirical results of learning performance are also discussed.