Vakkari, [Vak00a, Vak00b], also examined long running searches to examine how relevance
assessments changed over time. In his study he demonstrated that not only did subjects chose different
documents at different stages in their task, they also used different search tactics and strategies
38
.
Vakkari provided support for Spink's observation that high numbers of partial assessments correlates
with a lack of ability to discriminate relevant and non relevant. This may occur at the start of a search,
for example. He also found evidence to indicate that when a user has a good idea of what constitutes
relevant material he is less likely to make a high number of relevance assessments
These studies are important for RF because they point to the fact that not all relevance assessments are
equal: users make assessments for different reasons and with different amounts of knowledge. RF
techniques developed so far tend not to make these distinctions or incorporate this kind of knowledge.
8 Conclusion
RF has proved to be a useful and pragmatic solution to the uncertainty of describing an information
need. It has further, in test collection evaluations, been shown to be a relatively stable procedure: it
works in most cases, a wide range of algorithms give approximately the same performance and how the
algorithmic parameters should be set are fairly well understood. Although we have not discussed non
text documents, such as images or speech, in this paper the same basic principle of selecting good
discriminators of relevance can be used for different media to implement RF functionality.
The conceptual simplicity of RF users only have to recognise useful material, not describe it neatly
hides the complexity and variety of the query modification features behind the interface. However,
there is a growing awareness that RF is not sufficient on its own to improve retrieval. RF is useful in
that it is conceptually simple but it does not yet provide adequate support for the range of strategies and
tactics demonstrated by the user in research such as [Bat90]. RF may only be part of the interaction
process and will require integration with other functionalities.
Further, although RF is simple for the user to employ, the interaction decisions involved in RF can be
obscure. That is, RF generally does not give the user enough context on which to based their relevance
decisions, e.g. how many documents should be marked as relevant, how relevant should a document be
before being marked as relevant, what does not relevant mean? Although RF research has answers to
some of these questions (e.g. more relevance information is generally better), getting the user to provide
the necessary input data is not easy, and making the process of assessing relevance more difficult may
result in less interaction not more.
Therefore we argue that the strength of RF shown in non interactive situations should exploited in the
interactive situation by paying much more attention to the users of RF techniques and how they
incorporate RF into their searching. Finally, we note that RF is not only a potentially useful technique
for improving the quality of a searching but is also a very useful for technique for investigating how
people search. Only by studying how people actually interact with systems can we understand how to
build more usable and useful search systems.
Acknowledgements
We would like to express our thanks to Keith van Rijsbergen and Joemon Jose who gave many useful
comments on this survey. We would also like to acknowledge the helpful suggestions made by the
anonymous reviewer.
References
[Aal92] I. J. Aalbersberg. Incremental relevance feedback. Proceedings of the Fifteenth Annual
International ACM SIGIR Conference on Research and Development in Information Retrieval. pp 11
22. Copenhagen. 1992.
[BYRN99] R. Baeza Yates and B. Ribeiro Neto. Modern information retrieval. Addison Wesley.
1999.
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As measured using Kuhlthau's categorisation of searching, [Kuh91, Kuh93]
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