Improving an Information Retrieval System by Using Machine Learning to Improve User Relevance Feedback

Abstract: The aim of this thesis work is to improve the performance of an already existing information retrieval system that uses relevance feedback for performing query expansion. It is a constant goal to improve this system because the docu- ments that are retrieved are a base for various data analysis tasks. It is therefore important that the precision and re- call are high. A user can choose to give relevance feedback when executing a query, meaning the user can mark docu- ments in the search result as relevant or irrelevant and redo the search based on this feedback. The original query will then be expanded based on the user’s feedback. The ap- proach presented in this thesis uses the documents marked as relevant or irrelevant to train a classifier that can classify unknown documents from the search result as either rele- vant, irrelevant or unknown. The aim is to classify unknown documents and add them to the set of feedback documents that are used for the query expansion. The assumption that this thesis is based on is that the more feedback a user gives, the better the query expansion will perform. The system developed in this thesis is evaluated for the English language. The results in this thesis show that integrating the classifier in the existing system improved the perfor- mance in three out of four use cases. The existing system already has a good performance, but small improvements are important. It would therefore be beneficial to integrate it into the existing system.