Information and Recommender Systems

Table of contents

1. Cover
2. Title
3. Copyright
4. Introduction
5. 1 A Few Important Details Before We Begin
   1. 1.1. Information systems
   2. 1.2. Decision support systems
   3. 1.3. Recommender systems
   4. 1.4. Comparisons
   5. 1.5. Recommendation versus personalization
6. 2 Recommender Systems
   1. 2.1. Introduction
   2. 2.2. Classification of recommender systems
   3. 2.3. User profiles
   4. 2.4. Data mining
   5. 2.5. Content-based approaches
   6. 2.6. Collaborative filtering approaches
   7. 2.7. Knowledge-based approaches
   8. 2.8. Hybrid approaches
   9. 2.9. Other approaches
7. 3 Key Concepts, Useful Measures and Techniques
   1. 3.1. Vector space model
   2. 3.2. Similarity measures
   3. 3.3. Dimensionality reduction
   4. 3.4. Classification/clustering
   5. 3.5. Other techniques
   6. 3.6. Comparisons
8. 4 Practical Implementations
   1. 4.1. Commercial applications
   2. 4.2. Databases
   3. 4.3. Collaborative environments
   4. 4.4. Smart cities
   5. 4.5. Early warning systems
9. 5 Evaluating the Quality of Recommender Systems
   1. 5.1. Data sets, sparsity and errors
   2. 5.2. Measures
10. Conclusion
11. Bibliography
12. Index
13. End User License Agreement