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The Learning to Rank (L2R) research field has experienced a fast paced growthover the last few years, with a wide variety of benchmark datasets andbaselines available for experimentation. We here investigate the mainassumption behind this field, which is that, the use of sophisticated L2Ralgorithms and models, produce significant gains over more traditional andsimple information retrieval approaches. Our experimental results surprisinglyindicate that many L2R algorithms, when put up against the best individualfeatures of each dataset, may not produce statistically significantdifferences, even if the absolute gains may seem large. We also find that mostof the reported baselines are statistically tied, with no clear winner.

Is Learning to Rank Worth It? A Statistical Analysis of Learning to Rank Methods