Axiomatic Result Re-Ranking
Matthias Hagen, Michael Völske, Steve Göring, Benno Stein · 2016
We consider the problem of re-ranking the top-k documents returned by a retrieval system given some search query. This setting is common to learning-to-rank scenarios, and it is often solved with machine learning and feature weighting based on user preferences such as clicks, dwell times, etc. In this paper, we combine the learning-to-rank paradigm with the recent developments on axioms for information retrieval. In particular, we suggest to re-rank the top-k documents of a retrieval system using carefully chosen axiom combinations. In recent years, research on axioms for information retrieval has focused on identifying reasonable constraints that retrieval systems should fulfill. Researchers have analyzed a wide range of standard retrieval models for conformance to the proposed axioms and, at times, suggested certain adjustments to the models. We take up this axiomatic view---but, instead of adjusting the retrieval models themselves, we suggest the following innovation: to adopt the learning-to-rank idea and to re-rank the top-k results directly using promising axiom combinations. This way, we can turn every reasonable basic retrieval model into an axiom-based retrieval model. In large-scale experiments on the ClueWeb corpora, we identify promising axiom combinations for a variety of retrieval models. Our experiments show that for most of these models our axiom-based re-ranking significantly improves the original retrieval performance.