- You’ll reformulate the recommender problem to a ranking problem.
- You’ll look at Foursquare’s ranking method and how it uses multiple sources.
- You’ll go through the different types of Learning to Rank (LTR) algorithms and learn how to distinguish pointwise, pairwise, and listwise comparisons of ranks.
- You’ll learn about the Bayesian Personalized Ranking (BPR) algorithm, which is a promising algorithm to implement.
Are all these chapters on recommender algorithms starting to look the same? If so, you’re in luck, because now you’re going to start something completely different. Instead of focusing on recommendations as a rating prediction problem, it sometimes makes more sense to look at how the items should be stacked. The catalog item that the user would find most relevant is on top, the second one next, and so on. To define relevancy like this takes away the need to predict ratings. You don’t need to know how favorably users would rate something, only that they’d love it, or at least like it more than everything else that’s available.
Note
Keep in mind that the catalog of content might not contain anything the user would love, but even when that’s the case, you still want to provide a list of the best you can do with what you have.