Information retrieval (IR) is an important part of many tasks performed by people when they
use computers. However, most IR research and theory isolates the IR component from the
tasks performed by users. This is done by expressing user needs as a query performed on a
database. In contrast this dissertation investigates the design and evaluation of information
retrieval systems where the information retrieval mechanisms remain embedded in the user
tasks.
While there are a many different types of user tasks performed with computers we can specify
common requirements for the IR needed in most tasks. There are both user interface and
machine processing requirements. For user interfaces it is desirable if users interact directly
with information databases, keep control of the interaction and are able to perform IR in a
timely manner. Machine processing has to be within the capabilities of machines yet must fit
with human perceptions and has to be efficient in both storage and computation.
Given the overall requirements, the dissertation gives a particular implementation for how to
embed IR in tasks. The implementation uses a vector representation for objects and organises
the objects in a near neighbour data structure. Near neighbours are defined within the context
of the tasks the users wish to achieve. While the implementation could use many different
finding mechanisms, it emphasises a constructive solution building approach with localised
browsing in the database. It is shown how the IR implementation fits with the overall task
activities of the user.
Much of the dissertation examines how to evaluate embedded IR. Embedded IR requires
testing users' task performance in both real experiments and thought experiments.
Implementation is tested by finding known objects, by validating the machine representations
and their correspondence with human perceptions and by testing the machine performance of
the implementation.
Finally implications and extensions of the work arc explored by looking at the practicality of
the approach, other methods of investigation and the possibility of building dynamic learning
systems that improve with use.
| Identifer | oai:union.ndltd.org:ADTP/218861 |
| Date | January 1994 |
| Creators | Cox, Kevin Ross, n/a |
| Publisher | University of Canberra. Information Sciences & Engineering |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | ), Copyright Kevin Ross Cox |
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