Designing and Evaluating Intelligent Context-Aware Recommender Systems: Methodologies and Applications
Total Page：16
File Type：pdf, Size：1020Kb
Faculty of Engineering and Technology Doctoral Dissertation Designing and Evaluating Intelligent Context-Aware Recommender Systems: Methodologies and Applications Panayiotis Christodoulou Limassol, December 2017 1 CYPRUS UNIVERSITY OF TECHNOLOGY FACULTY OF ENGINEERING AND TECHNOLOGY DEPARTMENT OF ELECTRICAL ENGINEERING, COMPUTER ENGINEERING AND INFORMATICS Doctoral Dissertation Designing and Evaluating Intelligent Context-Aware Recommender Systems: Methodologies and Applications Panayiotis Christodoulou Limassol, December 2017 2 3 Copyrights Copyright © 2017 Panayiotis Christodoulou All rights reserved. The approval of the dissertation by the Department of Electrical Engineering, Computer Engineering and Informatics does not imply necessarily the approval by the Department of the views of the writer. 4 Achnowledgements: Obtaining a PhD degree is an interesting journey full of new discoveries and surprises along the way. Although for me this was not easy, the knowledge acquired along this way is completely worthy. Throughout this journey I never felt alone, thus I would like to express my sincere gratitude to those who helped me to achieve my goal. I had the honor of being supervised by Professor Andreas Andreou, his constant support, guidance, friendship, patience and encouragement always kept me on the right path. Also, I am really grateful to the Associate Professor Soterios Chatzis for his collaboration, support and help. Moreover, I would like to thank my colleagues Constantinos Stylianou, Andreas Christoforou and Charis Partaourides for their collaboration. My boundless love and appreciation goes to my wife Christiana Lambrianidou and my son, for their constant love and support through this journey. Finally, I would like to thank my parents Andreas Christodoulou and Theodosia, my brother Klitos Christodoulou and all my friends for supporting me during the more stressful periods and for encouraging me to pursue my interests. 5 ABSTRACT This research introduces new concepts and methodologies for Recommender Systems aiming to enhance the user experience and at the same time to improve the system’s accuracy by dealing with the challenges of RS. The thesis and the corresponding research is structured in three main parts. The first part of this thesis concentrates more on the development of new Multi-criteria RS to improve the accuracy and performance of RS. Our study examines solutions on how to deal with data sparsity, scalability issues and the cold-start problem by utilizing various techniques. The second part deals with the classification prediction problem. We propose a new methodology for developing hybrid models to improve the accuracy of classification models and thus provide better recommendations. The final part introduces a Recurrent Latent Variable framework based on a variational Recurrent Neural Network that deals with data sparsity and uncertainty met on session-based recommendations and sequence-based data. Experimentation was performed in all three parts mentioned and the results demonstrated the validity of the proposed methodologies when compared with state-of-the-art methods. Keywords: Multi-criteria Recommender Systems, Recommendations utilizing classification models, Session-based recommendations, Sequence-based data 6 PUBLICATIONS In the context of this thesis the following papers have been produced and published: 1. A dynamic Web Recommender System using Hard and Fuzzy K-modes clustering Christodoulou Panayiotis, Lestas Marios & Andreou S. Andreas 9th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, 2013, Paphos, Cyprus 2. Applying hard and fuzzy K-modes clustering for dynamic Web recommendations Christodoulou Panayiotis, Lestas Marios & Andreou S. Andreas Engineering Intelligent Systems, 2014, Volume 22, Issue 3-4, Pages 177-190 3. A Real-Time Targeted Recommender System for Supermarkets Christodoulou Panayiotis, Christodoulou Klitos & S. Andreou Andreas 19th International Conference on Enterprise Information Systems (ICEIS) 4. A Hybrid Prediction Model Integrating Fuzzy Cognitive Maps With Support Vector Machines Christodoulou Panayiotis, Christoforou Andreas & Andreou S. Andreas 19th International Conference on Enterprise Information Systems (ICEIS) 5. Improving the Performance of Classification Models with Fuzzy Cognitive Maps Christodoulou Panayiotis, Christoforou Andreas & Andreou S. Andreas S. 2017 IEEE Conference on Fuzzy Systems 6. A Variational Recurrent Neural Network for Session-Based Recommendations using Bayesian Personalized Ranking Christodoulou Panayiotis, Sotirios P. Chatzis & Andreou S. Andreas 26th International Conference on Information Systems Development 7 7. Recurrent Latent Variable Networks for Session-Based Recommendation Christodoulou Panayiotis, Sotirios P. Chatzis & Andreou S. Andreas 2nd Deep Learning technology for Recommender Systems - RecSys 2017 8. A Variational Latent Variable Model with Recurrent Temporal Dependencies for Session-Based Recommendation (VLaReT) Christodoulou Panayiotis, Sotirios P. Chatzis & Andreou S. Andreas Information Systems Development Methods, Tools and Management, Lecture Notes in Information Systems and Organisation 9. A Recurrent Latent Variable Model for Supervised Modeling of High- Dimensional Sequential Data (under review) Christodoulou Panayiotis, Sotirios P. Chatzis & Andreou S. Andreas Expert Systems With Applications 8 TABLE OF CONTENTS ABSTRACT ...................................................................................................................... 6 PUBLICATIONS .............................................................................................................. 7 TABLE OF CONTENTS .................................................................................................. 9 LIST OF TABLES .......................................................................................................... 14 LIST OF FIGURES ........................................................................................................ 16 LIST OF ABBREVIATIONS ......................................................................................... 18 Chapter 1: Introduction ............................................................................................... 19 Chapter 2: Overview of Recommender Systems ........................................................ 26 2.1 Introduction ...................................................................................................... 26 2.2 Basic Concepts of RS ....................................................................................... 27 2.2.1 Content-Based RS ..................................................................................... 27 2.2.1.1 Overview of CB systems ................................................................... 27 2.2.1.2 Challenges of CB systems ................................................................. 28 2.2.1.3 Dealing with challenges of CB systems ............................................ 28 2.2.2 Collaborative Filtering .............................................................................. 29 2.2.2.1 Memory-Based CF approaches ......................................................... 29 2.2.2.2 Model-Based CF approaches ............................................................. 31 2.2.2.3 Challenges of CF methods ................................................................. 32 2.2.2.4 Dealing with challenges of CF systems ............................................. 33 2.2.3 Hybrid Systems ......................................................................................... 35 2.2.3.1 Overview of Hybrid Systems ............................................................ 35 2.2.3.2 Dealing with challenges using Hybrid models .................................. 35 2.2.4 Overview ................................................................................................... 37 Chapter 3: Extensions & Trends on Recommender Systems ..................................... 39 9 3.1 Context-Aware Recommender Systems ........................................................... 39 3.2 Tagging Systems .............................................................................................. 41 3.3 Mobile Recommender Systems ........................................................................ 44 3.4 Group Recommendations ................................................................................. 45 3.5 Recommender Systems Using Data Mining Techniques ................................. 46 3.6 Multi-criteria and Multi-dimensional Recommender Systems ........................ 48 3.7 Recommender Systems in Cloud Computing .................................................. 49 3.8 Recommender Systems with Ontologies and Linked-Data .............................. 50 3.9 Deep Learning Recommender Systems ........................................................... 51 3.10 Session-based recommendations .................................................................. 52 3.11 Classification Models in RS ......................................................................... 53 3.12 User Privacy & Trust in Recommender Systems ......................................... 54 3.13 Shilling Recommendations ........................................................................... 55 3.14 Metrics .......................................................................................................... 56 3.14.1 Diversity and Novelty ..........................................................................