Recognising and recommending context in social web search

  • Authors:

  • Zurina Saaya;Barry Smyth;Maurice Coyle;Peter Briggs

  • Affiliations:

  • Clarity, Centre for Sensor Web Technologies, School of Computer Science and Informatics, University College Dublin, Ireland;Clarity, Centre for Sensor Web Technologies, School of Computer Science and Informatics, University College Dublin, Ireland;Clarity, Centre for Sensor Web Technologies, School of Computer Science and Informatics, University College Dublin, Ireland;Clarity, Centre for Sensor Web Technologies, School of Computer Science and Informatics, University College Dublin, Ireland

  • Venue:
  • UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
  • Year:
  • 2011

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Abstract

In this paper we focus on an approach to social search, HeyStaks that is designed to integrate with mainstream search engines such as Google, Yahoo and Bing. HeyStaks is motivated by the idea that Web search is an inherently social or collaborative activity. Heystaks users search as normal but benefit from collaboration features, allowing searchers to better organise and share their search experiences. Users can create and share repositories of search knowledge (so-called search staks) in order to benefit from the searches of friends and colleagues. As such search staks are community-based information resources. A key challenge for HeyStaks is predicting which search stak is most relevant to the users current search context and in this paper we focus on this so-called stak recommendation issue by looking at a number of different approaches to profling and recommending community-search knowledge.