期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：3
DOI：10.15680/ijircce.2015.0303070
出版社：S&S Publications
摘要：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 therelevance of search results, personalized search engines create user profiles to capture the users’ personal preferencesand as such identify the actual goal of the input query. Since users are usually reluctant to explicitly provide theirpreferences due to the extra manual effort involved, recent research has focused on the automatic learning of userpreferences from users’ search histories or browsed documents and the development of personalized systems based onthe learned user preferences. Most personalization methods focused on the creation of one single profile for a user andapplied the same profile to all of the user’s queries. We believe that different queries from a user should be handleddifferently because a user’s preferences may vary across queries. In this paper, we conduct extensive analyses andcomparisons 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 commercialsearch engine show that: (1) ontology performs better than session, query and task trails in determining usersatisfaction; (2) Ontology increases web page utilities of end users comparing to session, query and task trails ; (3) genericprogramming is more sensitive than other trail methods in measuring different ranking functions; (4) Query suggestionbased on ontology is a good complement of query suggestions based on session trail and click-through bipartite. Thefindings in this paper verify the need of extracting ontology from web search logs and enhance applications in search andrecommendation systems.
关键词：Semantic based search; Segmentation of Task; Task Analysis; Ontology based personalized search;Plug rising unauthorized access
