Inference of relevance for proactive information retrieval

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.