Applying taxonomic knowledge to Bayesian belief network for personalized search

Keyword-based search returns its results without concern for the information needs of users at a particular time. In general, search queries are too short to represent what users want, and thus it is necessary to more exactly represent the users’ intended semantics. Hence, our goal is to enrich the semantics of user-specific information (e.g., users’ queries and preferences) with a set of concepts for personalized search. To achieve this goal, we adopt a Bayesian belief network (BBN) as a strategy for personalized search since the Bayesian belief network provides a clear formalism for mapping user-specific information to its corresponding concepts. Nevertheless, as the concept layer of the

• Type: Conference
• Year: 2010
• Where: SAC
• Authors: Jae-won Lee, Han-joon Kim, Sang-goo Lee
• Keywords: Applied Computing, Bayesian Belief Network, Personalized Search, SAC 2010, User-specific Information