Dear Sir,<br> I am Pankaj Singhal from Jaipur, India. I
am very much interested and strongly looking forward in getting involved in this project Learning-to-Rank.<br><br>My previous experience in this field is
good. Last semester I did a similar job of ranking the URLs of the given
huge dataset based on their attribute values. The dataset consisted hundreds of thousands of URLs and
each url consisted of around 33000 features and a binary class label with +1 OR
-1 value. I applied the Decision Tree induction(GINI INDEX) Approach
for filtering out the URLs and then applying a RANKSUM[1] metric, which uses weighted sum approach, to rank
the URLs accordingly. <br><br>The current implementation involves firstly the unsupervised ranking of a query and then applying a supervised learning algorithm, SVM, on the first 'n' documents retrieved. <br>A similar approach can be incorporated while extending the problem of ranking with a better supervised learning algorithm and probabilistic model viz. Bayesian Belief Networks i.e. it can be applied after fetching 'n' documents from either of the two approaches, unsupervised ranking or SVM ranking. <br clear="all">
<br>Incorporating pairwise approach would also be a good idea, there are various algorithms available.<br><br><br><br><br>[1] - Rank-Order Weighting of Web Attributes for Website Evaluation - Mehri Saeid, Abdul Azim Abd Ghani, and Hasan Selamat<br>
<br><br>regards,<br clear="all"><br>-- <br>Pankaj Singhal<br>III Year, CSE<br>The LNMIIT, Jaipur, India.<br><br>Mob: +918875053936<br><br><br><br>