Best Thesis - 2012

Adaptive Retrieval, Composition & Presentation of Closed-Corpus and Open-Corpus Information

-Ben Steichen

Abstract: 

A key challenge for information access systems lies in their ability to deliver information that is most suited to a user’s needs, preferences and context. Personalised Information Retrieval (PIR) seeks to address this challenge by tailoring the selection of results to each individual user. Such PIR systems typically generate adaptive result rankings based on historic user interests or location properties. However, other considerations such as user needs, preferences or context are often neglected. Moreover, users are typically only presented with linear (monolingual) result rankings that do not provide any adaptive navigation support across different information sources. On the other hand, the field of Adaptive Hypermedia (AH) has inherently focused on generating non-linear, hyperlinked result compositions. This enables adaptive navigation and presentation support, allowing users a guided experience through an information space. Moreover, AH systems typically generate adaptive responses according to multiple considerations (also called personalisation “dimensions”), such as user needs, knowledge and context. However, AH techniques have typically only been applied across closed-corpus content bases, requiring substantial amounts of metadata. The key problem remains in providing such adaptive compositions across open-corpus information sources (in addition to closed corpora).

In order to address this problem, the thesis presents a novel compositional approach to open- and closed-corpus information retrieval and delivery through an innovative combination of Adaptive Hypermedia and Personalised Information Retrieval techniques. This technology enables the first dynamic integration and multidimensional adaptation of multilingual open and closed corpora. In particular, the contribution of the thesis is an extension of PIR and AH techniques to enable informed multiple adaptive query generation and adaptive result recomposition and presentation. This innovation is evaluated and validated through a series of case study implementations and evaluations, which show that the compositional approach successfully supports authentic user information needs in a personalised manner. In particular, it is shown that users are more efficient, effective and satisfied with the compositional approach compared to conventional information retrieval systems. Moreover, the approach is shown to be able to support multiple dimensions of adaptation, including user intent, language, knowledge, interface preferences and device capabilities.