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Abstract:
An often stated problem in the state-of-the-art web search is its lack of user
adaptation, as all users are presented with the same search results for a given
query string. A user submitting an ambiguous query such as ”java” with a strong
interest in traveling might appreciate finding pages related to the
Indonesian island Java. However, if the same user searched for programming
tutorials a few minutes ago, the situation would be completely different, and
call for programming-related results. Furthermore suppose our sample user
searches for ”java hashmap”. Again imposing her interest into traveling might
this time have the contrary effect and even harm the result quality. Thus the
effectiveness of a personalization of web search shows high variance in
performance depending on the query, the user and the search context. To this
end, carefully choosing the right personalization strategy in a
contextsensitive manner is critical for an improvement of search results.
In this thesis, we present a general framework that dynamically adapts the
query-result ranking to the different information needs in order to improve the
search experience for the individual user. We distinguish three different
search goals, namely whether the user re-searches known information, delves
deeper into a topic she is generally interested in, or satisfies an ad-hoc
information need. We take an implicit relevance feedback approach that makes
use of the user’s web interactions, however, vary what constitutes the examples
of relevant and irrelevant information according to the user’s search mode. We
show that incorporating user behavior data can significantly improve the
ordering of top results in a real web search setting.
