chapter five
5 Collaborative Filtering
This chapter covers
- How to design proper graph models for a collaborative filtering approach
- How to import existing (not-graph) datasets into the graph models designed
- How to implement working collaborative filtering recommendation engines
The content-based (also called content filtering or cognitive) approach to recommendations described in the previous chapter creates profiles for users and items to characterize them. The profiles allow systems to match users with relevant items. The general principle of content-based methods is to identify the common characteristics of items that have received favorable feedback from a user (a positive rating, a purchase, a click) and then recommend to this user new items that share these characteristics. Content-based strategies require gathering information that might not be readily available, easy to collect, or directly relevant.