Improving Query and Result List Adaptation in Personalized Multilingual Information Retrieval

Abstract

This research study aims to improve personalization in MIR systems, by improving the relevance of multilingual search results with respect to the user and not just the query. The study investigates how to model different aspects of a multilingual search user. Information about users can be demographic information, such as language and country, or information about the user’s search interests. This information can be gathered explicitly by asking the user to supply the required information or implicitly by inferring the information from the user’s search history. The study will then investigate how to exploit the modeled user information to personalize the user’s multilingual search by performing query and result list adaptation. The main research questions that are addressed in this study are: how to improve the relevance of search results with respect to individual users in PMIR and how to construct profiles that represent aspects and interests of a multilingual search user.