Browsing by Author "Kaski, Samuel, Prof., Helsinki Institute for Information Technology HIIT, Aalto University and University of Helsinki, Finland"

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  • Inference of relevance for proactive information retrieval
    (2013) Ajanki, Antti
     School of Science | Doctoral dissertation (article-based)
    Search engines have become very important as the amount of digital data has grown dramatically. The most common search interfaces require one to describe an information need using a small number of search terms, but that is not feasible in all situations. Expressing a complex query as precise search terms is often difficult. In the future, better search engines can anticipate user's goals and provide relevant results automatically, without the need to specify search queries in detail. Machine learning methods are important building blocks in constructing more intelligent search engines. Methods can be trained to predict which documents are relevant for the searcher. The prediction is based on recorded feedback or observations of how the user interacts with the search engine and result documents. If the relevance can be estimated reliably, interesting documents can be retrieved and displayed automatically. This thesis studies machine learning methods for information retrieval and new kinds of applications enabled by them. The thesis introduces relevance inference methods for estimating query terms from eye movement patterns during reading and for combining relevance feedback given on multiple connected data domains, such as images and their captions. Furthermore, a novel retrieval application for accessing contextually relevant information in the real world surroundings through augmented reality data glasses is presented, and a search interface that provides browsing cues by making potentially relevant items more salient is introduced. Prototype versions of the proposed methods and applications have been implemented and tested in simulation and user studies. The tests show that these methods often help the searcher to locate the right items faster than traditional keyword search interfaces would. The experimental results demonstrate that, by developing custom machine learning methods, it is possible to infer intent from feedback and retrieve relevant material proactively. In the future, applications based on similar methods have the potential to make finding relevant information easier in many application areas.