Practical Recommender Systems

Chapter 10. Content-based filtering

This chapter is all about content and users’ tastes:

• 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? ...