摘要：Web search engines provide users with a huge number of results for a submitted query. However, not all returned results are relevant to the user's needs. Personalized search aims at solving this problem by modeling search interests of the user in a profile and exploiting it to improve the search process. One of the challenges in search personalization is how to properly model user's search interests. Another challenge is how to effectively exploit these models to enhance the search quality. In this paper, an effective hybrid personalized re-ranking search approach is proposed by modeling user's search interests in a conceptual user profile, and then exploiting this profile in the re-ranking process. The user profile consists of concepts obtained by hierarchically classifying user's clicked search results into categories. These categories are extracted from the taxonomy of concepts called The Open Directory Project (ODP) where each concept represents a category. Additionally, each concept in the user profile consists of two types of documents; taxonomy document and viewed document. Taxonomy document is used to represent the user general interests as it contains information from web pages originally associated with such ODP category. Viewed document is used to represent the user specific interests as it contains information from web pages clicked by the user. Finally, the re-ranking process of search results is performed by semantically integrating user's general and specific interests from the user profile together with rankings of the traditional search engine. Experimental results show that semantic identification of user's search interests improves re-ranking quality by providing users with the most relevant results at the top of the search results list.
