Panagiotis Symeonidis is Adjunct Assistant Professor at the Aristotle University of Thessaloniki, Greece. He is the co-author of 2 international books, 18 journal papers, 4 book chapters and more than 28 articles in international conference proceedings. His articles have received almost 1400 citations from other scientific publications. He teaches courses on databases, data mining and data. For almost four years, he was the head of 1st EK (Laboratory Center) of Stavroupolis between September 2011 to July 2015. His research interests focus on recommender systems, social media in Web 2.0 and time-evolving online social networks.Andreas Zioupos has a B.Sc. degree in Mathematics and received his M.Sc. degree in Informatics & Management in 2015 from the Aristotle University of Thessaloniki, under the supervision of Dr. Panagiotis Symeonidis. He is an instructor for Google web tools and also has currently a contract as freelancer with the University of Piraeus on the project “Creating a framework for documentation, collection and disposal in the form of Linked Open Data from research results and official data of general government relating to domestic economic activity”. His research interests focus on data mining, recommender systems and dimensionality reduction methods.

“This carefully written book offers advanced undergraduates, graduate students, researchers and professionals a comprehensive overview of the general concepts and techniques (e.g., models and algorithms) related to matrix and tensor factorization in the field of recommender systems, with a rich blend of theory and practice. … I am definitely a recommender of this book!” (Bruno Carpentieri, Mathematical Reviews, August, 2017)

• Part I Matrix Factorization Techniques.- 1. Introduction.- 2. Related Work on Matrix Factorization.- 3. Performing SVD on matrices and its Extensions.- 4. Experimental Evaluation on Matrix Decomposition Methods.- Part II Tensor Factorization Techniques.- 5. Related Work on Tensor Factorization.- 6. HOSVD on Tensors and its Extensions.- 7. Experimental Evaluation on Tensor Decomposition Methods.- 8 Conclusions and Future Work.

Panagiotis Symeonidis is Adjunct Assistant Professor at the Aristotle University of Thessaloniki, Greece. He is the co-author of 2 international books, 18 journal papers, 4 book chapters and more than 28 articles in international conference proceedings. His articles have received almost 1400 citations from other scientific publications. He teaches courses on databases, data mining and data. For almost four years, he was the head of 1st EK (Laboratory Center) of Stavroupolis between September 2011 to July 2015. His research interests focus on recommender systems, social media in Web 2.0 and time-evolving online social networks.Andreas Zioupos has a B.Sc. degree in Mathematics and received his M.Sc. degree in Informatics & Management in 2015 from the Aristotle University of Thessaloniki, under the supervision of Dr. Panagiotis Symeonidis. He is an instructor for Google web tools and also has currently a contract as freelancer with the University of Piraeus on the project “Creating a framework for documentation, collection and disposal in the form of Linked Open Data from research results and official data of general government relating to domestic economic activity”. His research interests focus on data mining, recommender systems and dimensionality reduction methods.

“This carefully written book offers advanced undergraduates, graduate students, researchers and professionals a comprehensive overview of the general concepts and techniques (e.g., models and algorithms) related to matrix and tensor factorization in the field of recommender systems, with a rich blend of theory and practice. … I am definitely a recommender of this book!” (Bruno Carpentieri, Mathematical Reviews, August, 2017)

• Part I Matrix Factorization Techniques.- 1. Introduction.- 2. Related Work on Matrix Factorization.- 3. Performing SVD on matrices and its Extensions.- 4. Experimental Evaluation on Matrix Decomposition Methods.- Part II Tensor Factorization Techniques.- 5. Related Work on Tensor Factorization.- 6. HOSVD on Tensors and its Extensions.- 7. Experimental Evaluation on Tensor Decomposition Methods.- 8 Conclusions and Future Work.

Panagiotis Symeonidis is Adjunct Assistant Professor at the Aristotle University of Thessaloniki, Greece. He is the co-author of 2 international books, 18 journal papers, 4 book chapters and more than 28 articles in international conference proceedings. His articles have received almost 1400 citations from other scientific publications. He teaches courses on databases, data mining and data. For almost four years, he was the head of 1st EK (Laboratory Center) of Stavroupolis between September 2011 to July 2015. His research interests focus on recommender systems, social media in Web 2.0 and time-evolving online social networks.Andreas Zioupos has a B.Sc. degree in Mathematics and received his M.Sc. degree in Informatics & Management in 2015 from the Aristotle University of Thessaloniki, under the supervision of Dr. Panagiotis Symeonidis. He is an instructor for Google web tools and also has currently a contract as freelancer with the University of Piraeus on the project “Creating a framework for documentation, collection and disposal in the form of Linked Open Data from research results and official data of general government relating to domestic economic activity”. His research interests focus on data mining, recommender systems and dimensionality reduction methods.

“This carefully written book offers advanced undergraduates, graduate students, researchers and professionals a comprehensive overview of the general concepts and techniques (e.g., models and algorithms) related to matrix and tensor factorization in the field of recommender systems, with a rich blend of theory and practice. … I am definitely a recommender of this book!” (Bruno Carpentieri, Mathematical Reviews, August, 2017)

• Part I Matrix Factorization Techniques.- 1. Introduction.- 2. Related Work on Matrix Factorization.- 3. Performing SVD on matrices and its Extensions.- 4. Experimental Evaluation on Matrix Decomposition Methods.- Part II Tensor Factorization Techniques.- 5. Related Work on Tensor Factorization.- 6. HOSVD on Tensors and its Extensions.- 7. Experimental Evaluation on Tensor Decomposition Methods.- 8 Conclusions and Future Work.