Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Personalized Search Based on User Search Histories
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Predicting when browsing context is relevant to search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Anticipatory search: using context to initiate search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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In this paper, we investigate the problem of improving the relevance of a Web search engine by adapting it to the dynamic needs of the user. We examine a representative case of sudden information need change, namely the exposure of the user to news data. In our earlier work we showed that the majority of queries submitted by users after browsing documents in the news domain are related to the most recently browsed document. We explore several methods of biasing the search by performing query expansion and re-ranking of the search results of a major search engine for queries identified as good candidates for contextualization. We show that these methods highly increase the similarity between the obtained top 10 search results and the most recently browsed document.