Paper Review: Learning to Rank

I was looking for a paper which was related to a ranking mechanism in machine learning to assuage my curiosity about rank data in data mining. Although there are many research about machine learning and stuff, finally a particular learning named learning to rank came across to my mind. Hopefully this will enrich our knowledge, not only me but also all of the readers.

This article reviews a paper titled “A Short Introduction to Learning to Rank” written by Hang Li. The paper is laconic enough to be published in 6 pages. It consists of 6 sections which convey contents about background, process, technical sections, related approach, and so forth.

By bringing a good introduction, the paper is delivering lucid explanations and directions about the research. According to the flows, the paper gives good understanding of the mechanisms from the beginning and relates other discoveries. Turning to the details, it shows simple illustration of the process, brief description of formulation, related approach, and important algorithms. Finally, it sums up by future development which is still an enigma.

However, despite of positive comments, it exists some shortcomings such as details of technical algorithm, comprehensiveness of compared algorithms, and summary. Although the paper shows the mechanism of the algorithm, it does not appear deeply so still there is an enigma in what exactly it works. Specifically, real algorithm never appears publicly or even the pseudo code. Furthermore, the paper needs to extract more related algorithms since the readers would be glad to know the comprehensiveness which could be compared briefly. Finally, the papers should mention about the concise explanations which is required for closing statements.

 

 

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