If you run an e-commerce website a classical problem is to rank your product offering in the search page in a way that maximises the probability of your items being sold. For example if you are selling shoes you would like the first pair of shoes in the search result page to be the one that is most likely to be bought.
Thanks to the widespread adoption of machine learning it is now easier than ever to build and deploy models that automatically learn what your users like and rank your product catalog accordingly. In this blog post I'll share how to build such models using a simple end-to-end example using the movielens open dataset.
Read the full article here: http://www.alfredo.motta.name/learning-to-rank-with-python-scikit-learn/