Chapter 1. Understanding collective intelligence
Figure 1.1. A user may be influenced by other users either directly or through intelligence derived from the application by mining the data.
Figure 1.2. Three components to harnessing collective intelligence. 1: Allow users to interact. 2: Learn about your users in aggregate. 3: Personalize content using user interaction data and aggregate data.
Figure 1.3. Four pillars for user-centric applications
Figure 1.4. An example of a user-centric application—LinkedIn (www.linkedin.com)
Figure 1.5. Classifying user-generated information
Figure 1.6. This tag cloud from del.icio.us shows popular tags at the site.
Figure 1.7. Screen shot from Digg.com showing news items with the number of diggs for each
Figure 1.8. Screenshot from Yahoo! Music recommending songs of interest
Chapter 2. Learning from user interactions
Figure 2.1. Synchronous and asynchronous learning services
Figure 2.2. Architecture for embedding and deriving intelligence in an event-driven system
Figure 2.3. Architecture for embedding intelligence in a non-event-driven system
Figure 2.4. A user interacts with items, which have associated metadata.
Figure 2.5. The three sources for generating metadata about an item
Figure 2.6. Attribute hierarchy of a user profile
Figure 2.7. Term vector representation of text
Figure 2.8. Typical steps involved in analyzing text
Figure 2.9. Two dimensional vectors, v1 and v2