Information and Recommender Systems

Information is an element of knowledge that can be stored, processed or transmitted. It is linked to concepts of communication, data, knowledge or representation.  In a context of steady increase in the mass of information it is difficult to know what information to look for and where to find them. Computer techniques exist to facilitate this research and allow relevant information extraction.  Recommendation systems introduced the notions inherent to the recommendation, based, inter alia, information search, filtering, machine learning, collaborative approaches. It also deals with the assessment of such systems and has various applications.

Elsa Negro has a PhD in computer science and is professor at Paris-Dauphine University, France. She is particularly interested in decision-making information systems, recommendation systems, intelligent digital cities and crisis management.

Chapter 1. A Few Important Details Before We Begin  1

1.1. Information systems  1

1.2. Decision support systems  2

1.3. Recommender systems  3

1.4. Comparisons 4

1.5. Recommendation versus personalization 5

1.5.1. Recommendation 5

1.5.2. Personalization 6

Chapter 2. Recommender Systems  7

2.1. Introduction  8

2.2. Classification of recommender systems 9

2.2.1. Classification by score estimation method 9

2.2.2. Classification by data exploitation  10

2.2.3. Classification by objective  11

2.3. User profiles  11

2.4. Data mining  12

2.5. Content-based approaches 14

2.6. Collaborative filtering approaches 17

2.7. Knowledge-based approaches 20

2.8. Hybrid approaches 23

2.9. Other approaches  25

Chapter 3. Key Concepts, Useful Measures and Techniques 29

3.1. Vector space model  31

3.2. Similarity measures  31

3.2.1. Cosine similarity 31

3.2.2. Pearson correlation coefficient 32

3.2.3. Euclidean distance  33

3.2.4. Dice index  33

3.3. Dimensionality reduction  34

3.3.1. Principal component analysis  34

3.3.2. Singular value decomposition  35

3.3.3. Latent semantic analysis  36

3.4. Classification/clustering 36

3.4.1. Classification  36

3.4.2. Clustering 37

3.5. Other techniques  39

3.5.1. Term frequency-inverse document frequency (TF-IDF)  39

3.5.2. Association rules 40

3.6. Comparisons 41

Chapter 4. Practical Implementations  43

4.1. Commercial applications  43

4.1.1. Amazon.com  43

4.1.2. Netflix 45

4.2. Databases  46

4.3. Collaborative environments  48

4.4. Smart cities  49

4.5. Early warning systems  54

Chapter 5. Evaluating the Quality of Recommender Systems 57

5.1. Data sets, sparsity and errors 57

5.2. Measures  59

5.2.1. Accuracy 59

5.2.2. Other measures 63

Conclusion 65

Bibliography  67

Index  77

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