Hello all,<br><br>This is in reference to "Learning to Rank" Project
Idea. [I know, i made the entry a bit late, but hope you are still in
interest to help out]<br>I am looking for suggestions to help me
narrowing down the choices of algorithms. I had been readily surveying
on the referred algorithms for the purpose of choosing the right one. I
am mentioning here some of my doubts to discuss and make my concepts
clear about the algorithms, so i should end up choosing the most
suitable one. I am sure your input would be fruitful for me in
effectively drafting my proposal.<br>
The listnet looks like computationally more complex compared to ranknet.
So , is there any big advantage (in terms of improvement in ndcg/map)
to move to listnet AND the optimization suggested in the paper to look
for top one seems too simple. What will be the impact on accuracy and is
there any way to speed up /optimize listnet?<br>
For adarank i didnt understand how is it superior compared to linear regression??<br>I
was also trying to search for open-source package for training listnet
to save time and focus on more important aspects of library
enhancements, but didn't get any suitable one. However, FANN is still in
my to-check list, and meanwhile i was just experimenting to train
list-net in octave by reusing some of my ml-class code (an online course
by Professor Andrew Ng that i participated in). What is the quickest
way to understand the modularity to be-involved while implementing any
algorithm to serve the current need.<br>
<br>Thanks,<br><br>Regards,<br>Ashish Sadh<br>B.Tech, final year student.<br>Indian Institute of Information Technology, Allahabad, India.