We can turn a simple autoencoder into something sophisticated. Autoencoders discover a latent mapping z, which as a lower-dimensional representation of the input x, can be useful for pre-training networks and creating recommender models. However, how autoencoders compress information may come in millions of ways. To truly polish how z can be generated, we turn […]
Autoencoders are a simple neural network approach to recommendation
A tutorial to create a recommender pipeline with Kedro and MLFlow.
This is part 2 of my talk on recommenders. The presentation describes the intuitions of matrix factorization and how to implement it in R. I delivered this in the R-Users Group PH meetup group this November. If you wish to see some more recommender material in this blog, check this out:
Today I did the defense of my masters thesis entitled PRTNets: Cold-start recommendation using Pairwise Ranking and Transfer Networks. After nearly 5 years, I’m almost at the finish line! I never did notice that I spent more time in terms of semesters in UP than I did in ADMU. And this was because I changed […]
Join me in this roadtrip of different recommender algorithms.
Often, recommendations are printed as “You Might Like X because you watched Y.” This connection grounds the user from a prior experience and encourages her to consume the recommended content.