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  5. Demonstrating social search a la HeyStaks
 
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Demonstrating social search a la HeyStaks

Author(s)
Smyth, Barry  
Coyle, Maurice  
Briggs, Peter  
Uri
http://hdl.handle.net/10197/2289
Date Issued
2009-10
Date Available
2010-08-04T13:40:53Z
Abstract
For all the success of mainstream search engines there are a number of opportunities for improving on the conventional Web search user experience. In this short paper we consider the default assumption that search is solitary in nature, an isolated interaction between individual user and search engine. We highlight the value of a more collaborative approach to Web search and briefy present a novel add-on for mainstream search engines: HeyStaks (www.heystaks.com). It is designed to provide a more collaborative search experience, one in which recommendation technologies play a central role, by learning from the search experiences of groups of searchers in order to provide targeted recommendations during future search sessions.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Subjects

Search

Social search

Internet

Heystaks

Subject – LCSH
Internet searching
Web co-browsing
Recommender systems (Information filtering)
Online social networks
Language
English
Status of Item
Peer reviewed
Conference Details
Presented at the 1st International Workshop on Recommendation-Based Industry Applications at The 3rd ACM Conference on Recommender Systems (RecSys 2009), October 2009, New York, NY, USA.
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-sa/1.0/
File(s)
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Thumbnail Image
Name

HeyStaks Demov SUBMIT FINAL.pdf

Size

4.35 MB

Format

Adobe PDF

Checksum (MD5)

0791d9bed85c3ce61bf5c391a59778fd

Owning collection
Computer Science Research Collection
Mapped collections
CLARITY Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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