Abstract
Information Retrieval (IR) systems seek to find information which is relevant to a searcher’s information needs. Improving IR effectiveness using personalization has been a significant focus of research attention in recent years. However, in some situations there may be no opportunity to learn about the interests of a specific user on a certain topic. This is a particular problem for medical IR where individuals find themselves needing information on topics for which they have never previously searched. However, in all likelihood other users will have searched with the same information need previously. This presents an opportunity to IR researchers attempting to improve search effectiveness by exploiting previous user search behaviour. We describe a method to enhance IR in the medical domain based on recommender systems (RSs) by using a content-based recommender model in combination with a standard IR model. We use search behaviour data from previous users with similar interests to aid the current user to discover better search results. We demonstrate the effectiveness of this method using a test dataset collected as part of the EU FP7 Khresmoi project.
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Li, W., Jones, G.J.F. (2015). Enhancing Medical Information Retrieval by Exploiting a Content-Based Recommender Method. In: Mothe, J., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2015. Lecture Notes in Computer Science(), vol 9283. Springer, Cham. https://doi.org/10.1007/978-3-319-24027-5_12
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DOI: https://doi.org/10.1007/978-3-319-24027-5_12
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