Towards a reputation-based model of social web search

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.