Adaptive Personalization of Web Search

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