Recognising and recommending context in social web search

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Title: Recognising and recommending context in social web search
Authors: Saaya, Zurina
Smyth, Barry
Coyle, Maurice
Briggs, Peter
Permanent link: http://hdl.handle.net/10197/3446
Date: 11-Jul-2011
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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Copyright (published version): 2011 Springer
Keywords: Social searchContext recommendation
Subject LCSH: Internet searching
Online social networks
Recommender systems (Information filtering)
DOI: 10.1007/978-3-642-22362-4_25
Language: en
Status of Item: Peer reviewed
Is part of: Konstan, J.A. et al (eds.). User Modeling, Adaption and Personalization 19th International Conference, UMAP 2011, Girona, Spain, July 11-15, 2011. Proceedings
Conference Details: Paper presented at the International Conference on User Modeling, Adaptation and Personalization (UMAP-11). Girona, Spain. 11-15 July, 2011
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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