Linked Data principles have led to semantically interlink and connect different resourcesat data level
regardless the structure, authoring, location etc. Data available on the Web using Linked Data has
resulted in a global data space called the Web of Data. Moreover, thanks to the efforts of the scientific
community and the W3C Linked Open Data (LOD) project, more and more data have been published on
the Web of Data, helping its growth and evolution.
This book studies Recommender Systems that use LInked Data as a source for generating
recommendations exploiting the large amount of available resources and the relationships between
them. Firts, a comprehensive state of the art is preseted in order to indetify and study frameworks and
algorithms for RS that rely on Linked Data. Second a framework named AlLied taht makes available
implementations of the most used algortihms for resource recommendation based on Linked Data is
described.
This framework is inteded to use and test the recommendation algorithms in various domains and
contexts, and to analyze their behavior under different conditions. Accordingly the framework is suitable
to compare the results of these algorithms both in performance and relevance, and to enable the
development of innovative applications on top of it.
- Cover
- Title page
- Copyright page
- Contents
- Acknowledgements
- Chapter 1. Introduction
- 1.1 Contributions
- 1.2 Context
- Chapter 2. State of the art
- 2.1 Conceptual Foundation
- 2.2 Systematic Literature Review –SLR–
- 2.3 Results of the SLR
- 2.4 Summary
- Chapter 3. AlLied: A Framework for Executing Resource Recommendation Algorithms based on Linked Data
- 3.1 Architecture of the AlLied framework
- 3.2 Architecture design
- 3.3 Summary
- Chapter 4. AlLied implementation using graph-based algorithms
- 4.1 Knowledge Base Management
- 4.2 Recommender System Management
- 4.3 Presentation
- 4.4 Summary
- Chapter 5. AlLied implementation using machine learning algorithms
- 5.1 Knowledge Base Management
- 5.2 Recommender System Management layer
- 5.3 Presentation
- 5.4 Summary
- Chapter 6. Experimentation
- 6.1 Evaluation for the graph-based algorithms
- 6.2 Evaluation for the machine learning algorithms
- 6.3 Comparative evaluation graph-based algorithms vs machine learning algorithms
- 6.4 Evaluation of Performance
- 6.5 Concluding Remarks
- 6.6 Tools
- 6.7 Summary
- Chapter 7. Conclusions and Future Work
- 7.1 Conclusions
- 7.2 Proof of concept / use cases
- 7.3 Future Work
- 7.4 Summary
- Bibliography
- Appendix A. Selected Papers
- Appendix B. User Interfaces for the AlLied framework
- B.1 Mobile application that accesses to the RESTFul interface
- B.2 Desktop Application
- B.3 Web Client Application
- Appendix C. Complementary Documentation for the Machine-Learning based Implementation
- C.1 Common features for films in RS
- C.2 LODMatrixes
- Appendix D. User Interfaces of the survey for evaluating the AlLied framework
- About the authors
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