Towards Adaptive Information Retrieval - Step 1: Collecting Real Data
Udo Kruschwitz

This event took place on 25th May 2007 at 11:30am (10:30 GMT)
Knowledge Media Institute, Berrill Building, The Open University, Milton Keynes, United Kingdom, MK7 6AA

One of the most exciting areas of research in search engine technology and information retrieval is the move towards "adaptive" search systems. A particularly promising aspect of this wide field is to move log analysis right in the centre of attention. The challenge is to exploit the user interaction (as recorded in the log files) to make the search system adapt to the users' search behaviour. Instead of looking at the Web in general we are interested in smaller document collections with a more limited range of topics.

We are focusing on a search paradigm where automatically extracted domain knowledge is incorporated in a simple dialogue system in order to assist users in the search process. The challenge is to mine the log files in order to automatically improve the suggestions made by the system, in other words to "adapt" to the users' search behaviour. We are interested in a specific aspect of this search behaviour, namely the selection of query modification terms which provides us with "implicit feedback" from the users and should be sufficient to come up with a model to automatically adjust the domain knowledge without having to rely on other forms of explicit or implicit user feedback.

This whole process requires real data. We have made a start by running a prototype of our own search system that combines a standard search engine with automatically extracted domain knowledge. The system has been running on the University of Essex intranet for nearly a year now and we have collected more than 35,000 queries. The log files we keep collecting are an extremely valuable resource because they are a reflection of real user interests (different to TREC like scenarios which are always somewhat artificial). The data collected so far are a justification for a system that guides a user in the search process: more than 10% of user queries are query modification steps, i.e. the user either replaces the initial query or adds terms to the query to make it more specific. Adding a term happens more often than replacing the query with a completely new one. We also observe that a user is more likely to select one of the suggestions made by our search engine than modifying the query manually.

The talk will focus on our ongoing research and present some analysis of
the log files collected so far.

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