List of Figures

 

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