Chapter 12. Building a recommendation engine
This chapter covers
- Fundamentals for building a recommendation engine
- A content-based approach for building a recommendation engine
- A collaborative-based approach for building a recommendation engine
- Real-world case studies of Amazon, Google News, and Netflix
In recent years, increasing amount of user interaction has provided applications with a large amount of information that can be converted into intelligence. This interaction may be in the form of rating an item, writing a blog entry, tagging an item, connecting with other users, or sharing items of interest with others. This increased interaction has led to the problem of information overload. What we need is a system that can recommend or present items to the user based on the user’s interests and interactions. This is where personalization and recommendation engines come in.
Recommendation engines aim to show items of interest to a user. Recommendation engines in essence are matching engines that take into account the context of where the items are being shown and to whom they’re being shown.