Tourism Itinerary Recommender Systems - In the age of personalization and crowdsourcing

Introduction/ 7

1.1. Geographic Information Systems / 9

1.2. Location Based Services /12

1.3. Evolution from Monolithic to Distributed Architectures/ 15

1.4. Itinerary Recommender Systems/ 18

2. Context-Aware Personalized Recommendations/ 22

2.1. Collaborative Filtering Recommendations/ 22

2.2. Context-aware Recommendation Techniques / 26

3. Building Personalized Itineraries /30

3.1. Motivations for multi-objective genetic algorithms /30

3.2. Public Transport Route Finding using Genetic Algorithms /33

3.3. Building Itineraries using Multi-Objective Genetic Algorithms /47

4. Collecting Data using Crowdsourcing/ 53

4.1. Collaboration in Geographic Applications/ 53

4.2. Crowdsourcing Collaboration in GIS / 55

4.3. Assuring Robustness against Attacks in Crowdsourcing/ 60

5. Collaborative Solution for Itinerary Recommender Systems /68

5.1. Web Service Architecture for Itinerary Recommender Systems/ 68

5.2. Mobile Application Development Using Web Technologies/ 76

5.3. Mobile Itinerary Recommender Systems User Interface / 87

6. Further Improvements /106

6.1. Multi-Agent Systems/ 106

6.2. Multi-Agent Web Service Composition Framework /109

6.3. Assuring Reliability in Web Service Composition Applications/ 116

6.4. Geospatial Ontologies/ 119

7. Conclusions and Future Work /125

Bibliography /128

Annexes 1/ 142

Annexes 2/ 146

Annexes 3 / 149

Annexes 4/ 152