Consensus-based ranking of wikipedia topics
Waleed S. Nema, Yinshan Tang · Proceedings of the International Conference on Web Intelligence · 2017
To improve the effectiveness of users' information seeking experience in interactive web search we hypothesize how people might be influenced when making relevance judgment decisions by introducing the Consensus Theory & Relevance Judgment Model (CT&M). This is combined with a practical path to assess the extent of difference between suggestions of current search engines versus user expectations. A user-centered, evidence-based, phenomenology approach is used to improve on Google PageRank (GPR) in two ways. The first by biasing GPR's equal navigation probability assumption using (f)actual usage stats as implicit user consensus which leads to the StatsRank (SR) algorithm. Secondly, we aggregate users' explicit ranking to derive Consensus Rank (CR) which is shown to predict individual user ranking significantly better than GPR and meta-search of modern search engines Google and Yahoo/Bing real-time.