Chapter 1. Understanding collective intelligence
Table 1.1. Some of the ways to harness collective intelligence in your application
Table 1.2. Seven principles of Web 2.0 applications
Chapter 2. Learning from user interactions
Table 2.1. Summary of services that a typical application-embedding intelligence contains
Table 2.2. Examples of user-profile attributes
Table 2.3. The many ways users provide valuable information through their interactions
Table 2.4. Dataset with small number of attributes
Table 2.5. Dataset with large number of attributes
Table 2.6. Sparsely populated dataset corresponding to term vectors
Table 2.7. Ratings data used in the example
Table 2.8. Dataset to describe photos
Table 2.9. Normalized dataset for the photos using raw ratings
Table 2.10. Item-to-item using raw ratings
Table 2.11. Normalized rating vectors for each user
Table 2.12. User-to-user similarity table
Table 2.13. Normalized matrix for the correlation computation
Table 2.14. Correlation matrix for the items
Table 2.15. Normalized rating vectors for each user
Table 2.16. Correlation matrix for the users
Table 2.17. Normalized matrix for the adjusted cosine-based computation
Table 2.18. Similarity between items using correlation similarity
Table 2.19. Normalized rating vectors for each user
Table 2.20. Normalizing the vectors to unit lengthr
Table 2.21. Adjusted cosine similarity matrix for the users
Table 2.22. Bookmarking data for analysis
Table 2.23. Adjusted cosine similarity matrix for the users