- You’ll be introduced to content-based filtering.
- You’ll learn how to construct user and content profiles.
- You’ll learn to extract information from descriptions using term fequency-inverse document frequency (TF-IDF) and latent Dirichlet allocation (LDA) to create content profiles.
- You’ll implement content-based filtering using descriptions of films in MovieGEEKs site.
In previous chapters, you saw that it’s possible to create recommendations by focusing only on the interactions between users and content (for example, shopping basket analysis or collaborative filtering). Although those work nicely, what about the things that you know about the content? For a movie that can include categories such as genres, actors, and directors. In other sites, it can be things such as clothing sizes and colors, or engine sizes for cars. Can you call a recommender system good if it doesn’t take those things into account?
The answer is “YES!” as you’ve seen in the previous chapters, but it still seems as if you’re missing something or losing out on certain information. I’ll try to make up for that because this chapter covers what you know about content and users’ tastes.