Abstract
Older studies have proved that when searching information on the Web, users tend to write short queries, unconsciously trying to minimize the cognitive load. However, as these short queries are very ambiguous, search engines tend to find the most popular meaning – someone who does not know anything about cascading stylesheets might search for a music band called css and be very surprised about the results. In this paper we propose a method which can infer additional keywords for a search query by leveraging a social network context and a method to build this network from the stream of user’s activity on the Web.
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Kramár, T., Barla, M., Bieliková, M. (2010). Disambiguating Search by Leveraging a Social Context Based on the Stream of User’s Activity. In: De Bra, P., Kobsa, A., Chin, D. (eds) User Modeling, Adaptation, and Personalization. UMAP 2010. Lecture Notes in Computer Science, vol 6075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13470-8_37
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DOI: https://doi.org/10.1007/978-3-642-13470-8_37
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