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Recognising and Recommending Context in Social Web Search
Author(s)
Date Issued
2011-06-28
Date Available
2012-10-11T13:51:33Z
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.
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.
Sponsorship
Science Foundation Ireland
Other Sponsorship
This work is supported by Science Foundation Ireland under grant 07/CE/I1147, HeyStaks Technologies Ltd, Ministry of Higher Education Malaysia and Universiti Teknikal Malaysia Melaka.
Type of Material
Conference Publication
Publisher
Springer
Copyright (Published Version)
2011 Springer
Subject – LCSH
Internet searching
Online social networks
Recommender systems (Information filtering)
Language
English
Status of Item
Peer reviewed
Journal
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
International Conference on User Modeling, Adaptation and Personalization (UMAP-11), Girona, Spain, July 11-15, 2011
ISBN
978-3-642-22361-7
This item is made available under a Creative Commons License
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Format
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