%0 Conference Paper %1 mcnally2010towards %A McNally, Kevin %A O'Mahony, Michael P. %A Smyth, Barry %A Coyle, Maurice %A Briggs, Peter %B Proceedings of the 15th international conference on Intelligent user interfaces %C New York, NY, USA %D 2010 %I ACM %K collaborative heystaks reputation search social web alexandria %P 179--188 %R 10.1145/1719970.1719996 %T Towards a reputation-based model of social web search %U http://doi.acm.org/10.1145/1719970.1719996 %X While web search tasks are often inherently collaborative in nature, many search engines do not explicitly support collaboration during search. In this paper, we describe HeyStaks (www.heystaks.com), a system that provides a novel approach to collaborative web search. Designed to work with mainstream search engines such as Google, HeyStaks supports searchers by harnessing the experiences of others as the basis for result recommendations. Moreover, a key contribution of our work is to propose a reputation system for HeyStaks to model the value of individual searchers from a result recommendation perspective. In particular, we propose an algorithm to calculate reputation directly from user search activity and we provide encouraging results for our approach based on a preliminary analysis of user activity and reputation scores across a sample of HeyStaks users. %@ 978-1-60558-515-4

@inproceedings{mcnally2010towards, abstract = {While web search tasks are often inherently collaborative in nature, many search engines do not explicitly support collaboration during search. In this paper, we describe HeyStaks (www.heystaks.com), a system that provides a novel approach to collaborative web search. Designed to work with mainstream search engines such as Google, HeyStaks supports searchers by harnessing the experiences of others as the basis for result recommendations. Moreover, a key contribution of our work is to propose a reputation system for HeyStaks to model the value of individual searchers from a result recommendation perspective. In particular, we propose an algorithm to calculate reputation directly from user search activity and we provide encouraging results for our approach based on a preliminary analysis of user activity and reputation scores across a sample of HeyStaks users.}, acmid = {1719996}, added-at = {2012-09-28T12:28:30.000+0200}, address = {New York, NY, USA}, author = {McNally, Kevin and O'Mahony, Michael P. and Smyth, Barry and Coyle, Maurice and Briggs, Peter}, biburl = {https://www.bibsonomy.org/bibtex/2877ae3a66485776de88ba99951d9af2c/jaeschke}, booktitle = {Proceedings of the 15th international conference on Intelligent user interfaces}, doi = {10.1145/1719970.1719996}, interhash = {039d613f3fae6adab294e4b52f1ecb0e}, intrahash = {877ae3a66485776de88ba99951d9af2c}, isbn = {978-1-60558-515-4}, keywords = {collaborative heystaks reputation search social web alexandria}, location = {Hong Kong, China}, numpages = {10}, pages = {179--188}, publisher = {ACM}, timestamp = {2014-07-28T15:57:31.000+0200}, title = {Towards a reputation-based model of social web search}, url = {http://doi.acm.org/10.1145/1719970.1719996}, year = 2010 }