Chapter 2. Learning from user interactions
This chapter covers
- Architecture for applying intelligence
- Basic technical concepts behind collective intelligence
- The many forms of user interaction
- A working example of how user interaction is converted into collective intelligence
Through their interactions with your web application, users provide a rich set of information that can be converted into intelligence. For example, a user rating an item provides crisp quantifiable information about the user’s preferences. Aggregating the rating across all your users or a subset of relevant users is one of the simplest ways to apply collective intelligence in your application.
There are two main sources of information that can be harvested for intelligence. First is content-based—based on information about the item itself, usually keywords or phrases occurring in the item. Second is collaborative-based—based on the interactions of users. For example, if someone is looking for a hotel, the collaborative filtering engine will look for similar users based on matching profile attributes and find hotels that these users have rated highly. Throughout the chapter, the theme of using content and collaborative approaches for harvesting intelligence will be reinforced.