Towards a reputation-based model of social web search Authors: Kevin McNally, Michael P. O’Mahony, Barry Smyth, Maurice Coyle, Peter Briggs Keywords: collaborative web search, heystaks, reputation model
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.
The reputation system posed by the authors could easily underlie a collaborative approach to creation of a topic map.
Think collections not normally accessed by web search engines, The National Archives (U.S.) and similar document collections.
Reputation + trails + subject identity = Hard to Beat.
See www.heystaks.com as a starting point.