Using the current browsing context to improve search relevance

ABSTRACT

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.

REFERENCES

• 1: S. Cucerzan. Large-scale named entity disambiguation based on Wikipedia data. In EMNLP-CoNLL'07, pages 708--716, 2007.
• 2: Mandar Rahurkar , Silviu Cucerzan, Predicting when browsing context is relevant to search, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, July 20-24, 2008, Singapore, Singapore [doi>10.1145/1390334.1390532]
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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Query formulation

General Terms:
Algorithms, Experimentation, Measurement

Keywords:
contextualized, query expansion, query logs, web search