Unbiased Pairwise Approach toward Learning-to-Rank: An Empirical Study

Abstract

With the bloom of information technology in recent decades, people are constantly being exposed to a huge amount of information. Learning-to-rank comes out as one of the solutions to ease out the mentioned obstacle by trying to rearrange objects according to their degrees of importance or relevance. This solution usually applies machine learning techniques to construct ranking models in information retrieval systems. The aim of this study is to explore and experiment the existing learning-to-rank approaches with real-life logs data. The study also includes estimating and minimizing the bias noise found in the click-through data of the recorded logs. Evaluation results have presented the advantage and disadvantage of the experimented approaches in realistic settings.