Applying Taxonomic Knowledge and Semantic Collaborative Filtering to Personalized Search: A Bayesian Belief Network Based Approach

Keyword-based search exploits the exact match between the index terms of a query and documents. Thus, some documents, although they are relevant to the given query, may not be returned to users unless the documents include the index terms of the query. Some search engines use the authority of documents, which is derived from the links of documents, to help keyword-based search provide more accurate search results. However, unlike the Web documents, if the links between documents do not exist, it is difficult to exploit the authority for ranking documents. In this paper, our goals are to derive the implicit authority of documents that do not have explicit links through semantic collaborative filtering (SCF), and to retrieve documents that are semantically related to the given query. To achieve these goals, we represent users' preferences, queries and documents with their corresponding concepts by extending a Bayesian belief network. It is because the Bayesian belief network provides a clear formalism for mapping the users' preferences, queries and documents to their corresponding concepts. The concepts are extracted from a taxonomic knowledgebase such as the Open Directory Project Web directory. In our experiment, we have shown that the extended Bayesian belief network using taxonomic knowledge outperforms the conventional approaches for personalized search.