Graph-Powered Machine Learning

6 Session-based recommendations

This chapter covers

• Implementing recommendation systems by using session data
• Designing graph models for session-based recommendation engines
• Importing existing datasets into the graph models

Chapters 4 and 5 introduced two of the most common approaches to implementing recommendation engines: content-based and collaborative filtering. The advantages of each approach were highlighted, but several drawbacks also emerged during the discussion. Notably, these techniques require information about users that is not always available. This chapter covers another approach to recommendations that is useful when it is difficult or impossible to get access to user interaction history or other details about the users. In ...