[Xapian-devel] [xapian] GSoC - Learning to Rank, Introduction and some Ideas

[Parth Gupta]

Hi Mudit,

Good to know about you.

>
> I successfully completed my *Google Summer of Code - 2011* for the *Center
> for the study of Complex systems - University of Michigan*. I implemented
> various *algorithms (ant colony, random walk etc.)* related to
> computational intelligence in Repast S (*Coded in Groovy, Java*) and
> wrote *extensive documentations and tutorial* for the related models with
> *literature reviews* on the topics. My *contributions to Repast S was a
> part of the latest release of the software*. The detailed documentation
> and code can be found here:
> http://code.google.com/p/cscs-repast-demos/wiki/Mudit I have also worked
> on various projects related to implementation of Machine Learning and
> Bio-Inspired Evolutionary Algorithms.You can check the code and some
> documentation on the same on code.google.com my user profile is :
> http://code.google.com/u/110675325175605367090/
>

Seems your previous experience with machine learning will help you.


>
> I am interested in applying for the project - *"Learning to Rank*". I
> have read the pointers on the ideas page and some literature about it. I
> was thinking, based on my literature review, that something on the lines on
> Multi-layer Perceptron network with Ant Colony Optimization or an Improved
> random Forest could be a good option. I selected the same because of my
> experience on the topic. Although any further details/pointers to the
> projects would be greatly appreciated. I would also like to request you to
> please let me know about any specific detail related to the project that is
> required in the proposal (apart from the ones mentioned on the page)
>

There have been plenty of algorithms proposed in the recent past. Based on
your choice of ML technique, you can choose one. As you are interested in
Neural Net based approaches, ListNet [1], RankNet [2], ListMLE [3],
LamdaRank [4] can be of your interest and if you want to explore the random
forests based approaches then [5] can be checked out.

[1] Learning to rank: from pairwise approach to listwise approach
[2] Learning to Rank using Gradient Descent
[3] Listwise Approach to Learning to Rank - Theory and Algorithm
[4] Learning to Rank with Nonsmooth Cost Functions
[5] Learning to rank with extremely randomized trees


Regards,
Parth.



> Thank you for your time. Hope to hear from you soon.
>
> Best Regards,
>
> Mudit Raj Gupta
>
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