Personalized Search of User Search Behaviour with Ontology
In this paper, we introduce “Ontology and generic programming” to understand user search behaviors. Personalized search is an important research area that aims to resolve the ambiguity of query terms. To increase the relevance of search results, personalized search engines create user profiles to capture the users’ personal preferences and as such identify the actual goal of the input query. Since users are usually reluctant to explicitly provide their preferences due to the extra manual effort involved, recent research has focused on the automatic learning of user preferences from users’ search histories or browsed documents and the development of personalized systems based on the learned user preferences. Most personalization methods focused on the creation of one single profile for a user and applied the same profile to all of the user’s queries. We believe that different queries from a user should be handled differently because a user’s preferences may vary across queries. In this paper, we conduct extensive analyses and comparisons to evaluate the effectiveness of ontology in several search applications: determining user satisfaction, predicting user search interests, and suggesting related queries. Experiments on large scale datasets of a commercial search engine show that: (1) ontology performs better than session, query and task trails in determining user satisfaction; (2) Ontology increases web page utilities of end users comparing to session, query and task trails ; (3) generic programming is more sensitive than other trail methods in measuring different ranking functions; (4) Query suggestion based on ontology is a good complement of query suggestions based on session trail and click-through bipartite. The findings in this paper verify the need of extracting ontology from web search logs and enhance applications in search and recommendation systems.