Modeling user and result reputation in collaborative web search

Abstract

Employing collaborative recommendation techniques allows for personalization of search results in social web search. If recommendations arise from past user activity, the expertise of those users driving the recommendation process can play an important role when it comes to ensuring recommendation quality. Hence the reputation of users is important, in addition to result relevance as traditionally considered in web search. In this paper we explore this concept of reputation; specifically, investigating how reputation can enhance the recommendation engine at the core of the HeyStaks social search utility. We evaluate a number of different reputation models in the context of the HeyStaks system, and demonstrate how incorporating reputation into the recommendation process can enhance the relevance of results recommended by HeyStaks.