In this post we’re going to do a bunch of cool things following up on the last post introducing implicit matrix factorization. We’re going to explore Learning to Rank, a different method for implicit matrix factorization, and then use the library LightFM to incorporate side information into our recommender. Next, we’ll use scikit-optimize to be smarter than grid search for cross validating hyperparameters. Lastly, we’ll see that we can move beyond simple user-to-item and item-to-item ...
