People often struggle with getting good movie recommendations. The usual way to suggest movies is by using math to find patterns. But this method has a big problem: it can't handle complex requests. For example, it can't easily find a movie that is both a comedy and an action film but not a romance.
Imagine trying to find the perfect movie to watch. It's not just about finding any movie, but one that fits your unique tastes. This is where personalized recommendations come in. Traditionally, these systems struggle with understanding complex relationships between items. For instance, they might n
Geometric embeddings have been in the spotlight lately for their natural ability to represent relationships using containment. One popular method is box embeddings, where objects are depicted as n-dimensional hyperrectangles. These boxes are special because they can be intersected easily and their v
Imagine you're looking for movies on a streaming service. You want comedies, but they shouldn't be romances, and they have to be British. This is a compositional query, a bit more complex than just finding comedies. Currently, machine learning uses dot-products to connect items like movies to attri
