Abstract

Personalization in information retrieval aims at improving the user’s experience by incorporating the user subjectivity into the retrieval methods and models. The exploitation of implicit user interests and preferences has been identified as an important direction to enhance current mainstream retrieval technologies and anticipate future limitations as worldwide content keeps growing, and user expectations keep rising. Without requiring further efforts from users, personalization aims to compensate the limitations of user need representation formalisms (such as the dominant keyword-based or document-based) and help handle the scale of search spaces and answer sets, under which a user query alone is often not enough information for the system to provide effective results. However, the general set of user interests that a retrieval system can learn over a period of time, and bring to bear in a specific retrieval session, can be fairly vast, diverse, and to a large extent unrelated to a particular user search in process. This means that even on the basis of correctly learned user preferences, the system could make wrong guesses or get intrusive. Rather than introducing all user preferences en bloc, an optimum search adaptation could be achieved if the personalization system was able to select only those preferences which are pertinent to the ongoing user actions. In other words, although personalization alone is a key aspect of

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