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Using Topic-Specific Ranks to Personalize Web Search
Abstract
This chapter introduces a personalized ranking function as a means of offering Web information seekers with search results that satisfy their particular interests. It argues that users’ preferences can be accurately identified based on the semantic analysis of their previous searches and that learnt user preferences can be fruitfully employed for personalizing search results. In this respect, we introduce a ranking formula that encapsulates the user’s interests in the process of ordering retrieved results so as to meet the user’s needs. For carrying out our study we relied on a lexical ontology that encodes a number of concepts and their interrelations and which helps us determine the semantics of both the query keywords and the query matching pages. Based on the correlation between the query and document semantics, our model decides upon the ordering of search results so that these are personalized.
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