Abstract
In this paper we focus on one particular implementation of social search, namely HeyStaks, which combines ideas from web search, content curation, and social networking to make recommendations to users, at search time, based on topics that matter to them. The central concept in HeyStaks is the search stak. Users can create and share staks as a way to curate their search experiences. A key problem for HeyStaks is the need for users to pre-select their active stak at search time, to provide a context for their current search experience so that HeyStaks can index and store what they find. The focus of this paper is to look at how machine learning techniques can be used to recommend a suitable active stak to the user at search time automatically.
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© 2012 Springer-Verlag London
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Saaya, Z., Schaal, M., Coyle, M., Briggs, P., Smyth, B. (2012). A Comparison of Machine Learning Techniques for Recommending Search Experiences in Social Search. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXIX. SGAI 2012. Springer, London. https://doi.org/10.1007/978-1-4471-4739-8_14
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DOI: https://doi.org/10.1007/978-1-4471-4739-8_14
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