Chapter 6. AI for content curation and community building

 

This chapter covers

  • Using recommender systems to suggest engaging content and products
  • Understanding the two approaches to recommender systems: content and community-based
  • Understanding the drawbacks of algorithmic recommendations
  • Case study: using recommender systems to save $1 billion in churn

Recommender systems are the workhorse behind today’s “personalized” experiences, and a fundamental tool to help consumers navigate huge catalogs of media, products, and clothes. Anytime you click a related offer from Amazon or check out a suggested movie from Netflix, these companies are taking advantage of recommender systems to drive user engagement and top-line growth. Without them, navigating the vastness of products and digital content available on the internet would be simply impossible.

In the case study at the end of the chapter, you’ll see how Netflix believes that its recommender system has been saving the company more than $1 billion each year since 2015.

6.1 The curse of choice

6.2 Driving engagement with recommender systems

6.2.1 Content-based systems beyond simple features

6.2.2 The limitations of features and similarity

6.3 The wisdom of crowds: collaborative filtering

6.4 Recommendations gone wrong

6.4.1 The recommender system dream

6.5 Case study: Netflix saves $1 billion a year

6.5.1 Netflix’s recommender system

6.5.2 Recommendations and user experience

6.5.3 The business value of recommendations

6.5.4 Case questions

6.5.5 Case discussion

Summary