Francesco Ricci is full professor at the Faculty of Computer Science, Free University of Bozen-Bolzano. F. Ricci has established in Bolzano a reference point for the research on Recommender Systems. He has co-edited the Recommender Systems Handbook (Springer 2011, 2015), and has been actively working in this community as President of the Steering Committee of the ACM conference on Recommender Systems (2007-2010). He was previously (from 2000 to 2006) senior researcher and the technical director of the eCommerce and Tourism Research Lab (eCTRL) at ITC-irst (Trento, Italy). From 1998 to 2000 he was system architect in the Research and Technology Department (Process and Reuse Technologies) of Sodalia s.p.a. Francesco Ricci is author of more than two hundred fifty refereed publications and, according to Google Scholar, has H-index 57 and around 22000 citations.Lior Rokach is a data scientist and a professor of Software and Information Systems Engineering (SISE) atBen-Gurion University of the Negev (BGU).  His research interests lie in the design, development, and analysis of Machine Learning and Data Mining algorithms and their applications in Recommender Systems, Cyber Security and Medical Informatics. Rokach has co-founded four AI companies and has been awarded 22 patents for his inventions in AI and information technology. Prof. Rokach is the author of over 300 peer-reviewed papers in leading journals and conference proceedings. He is also the author of six books and the editor of three books.Bracha Shapira is a professor of Software and Information Systems Engineering (SISE) at Ben-Gurion University of the Negev (BGU), and an active data scientist. She has been leading research projects at the Deutsche  Telekom lab at Ben-Gurion University, and is a  member of the Cyber at BGU center, where she applies machine learning methods to many domains, including recommender systems, cyber security  and medical informatics. She has published more than 200 papers in leading journals and conferences and has been awarded more than 20 patents for her inventions.

• Preface.- Introduction.- Part 1: General Recommendation Techniques.- Trust Your Neighbors: A Comprehensive Survey of Neighborhood-based Methods for Recommender Systems (Desrosiers).- Advances in Collaborative Filtering (Koren).- Item Recommendation from Implicit Feedback (Rendle).- Deep Learning for Recommender Systems (Zhang).- Context Aware Re commender Sytems : From Foundatiom to Recent Developments (Bauman).- Semantics and Content-based Recommendations (Musto).- Part 2: Special Recommendation Techniques.- Session-based Recommender Systems (lannoch)..- Adversarial Recommender Systems: Attack,Defense, and Advances (Di Nola).- Group Recommender Systems: Beyond Preferance Aggregation (Masthoff).- People-to-People Reciprocal Recommenders (Koprinska).- Natural Language Processing for Recommender Systems (Sar-Shalom).- Design and Evaluation of Cross-domain Recommender Systems (Cremonesi).- Part 3: Value and Impact of Recommender Systems.- Value and Impact of Recommender Systems (Zanker).- Evaluating Recommender Systems (Shani).- Novelty and Diversity in Recommender Systems (Castells).- Multistakeholder Recommender Systems (Burke).- Fairness in Recommender Systems (Ekstrand).- Part 4: Human Computer Interaction.- Beyond Explaining Single Item Recommendations (Tintarev).- Personality and Recommender Systems (Tkalčič).- Individual and Group Decision Making and Recommender Systems (Jameson).- Part 5: Recommender Systems Applications.- Social Recommender Systems (Guy).- Food Recommender Systems (Trattner).- Music Recommendation Systems: Techniques, Use Cases, and Challenges (Schedl).- Multimedia Recommender Systems: Algorithms and Challenges (Deldjoo).- Fashion Recommender Systems (Dokoohaki).

Francesco Ricci is full professor at the Faculty of Computer Science, Free University of Bozen-Bolzano. F. Ricci has established in Bolzano a reference point for the research on Recommender Systems. He has co-edited the Recommender Systems Handbook (Springer 2011, 2015), and has been actively working in this community as President of the Steering Committee of the ACM conference on Recommender Systems (2007-2010). He was previously (from 2000 to 2006) senior researcher and the technical director of the eCommerce and Tourism Research Lab (eCTRL) at ITC-irst (Trento, Italy). From 1998 to 2000 he was system architect in the Research and Technology Department (Process and Reuse Technologies) of Sodalia s.p.a. Francesco Ricci is author of more than two hundred fifty refereed publications and, according to Google Scholar, has H-index 57 and around 22000 citations.Lior Rokach is a data scientist and a professor of Software and Information Systems Engineering (SISE) atBen-Gurion University of the Negev (BGU).  His research interests lie in the design, development, and analysis of Machine Learning and Data Mining algorithms and their applications in Recommender Systems, Cyber Security and Medical Informatics. Rokach has co-founded four AI companies and has been awarded 22 patents for his inventions in AI and information technology. Prof. Rokach is the author of over 300 peer-reviewed papers in leading journals and conference proceedings. He is also the author of six books and the editor of three books.Bracha Shapira is a professor of Software and Information Systems Engineering (SISE) at Ben-Gurion University of the Negev (BGU), and an active data scientist. She has been leading research projects at the Deutsche  Telekom lab at Ben-Gurion University, and is a  member of the Cyber at BGU center, where she applies machine learning methods to many domains, including recommender systems, cyber security  and medical informatics. She has published more than 200 papers in leading journals and conferences and has been awarded more than 20 patents for her inventions.

• Preface.- Introduction.- Part 1: General Recommendation Techniques.- Trust Your Neighbors: A Comprehensive Survey of Neighborhood-based Methods for Recommender Systems (Desrosiers).- Advances in Collaborative Filtering (Koren).- Item Recommendation from Implicit Feedback (Rendle).- Deep Learning for Recommender Systems (Zhang).- Context Aware Re commender Sytems : From Foundatiom to Recent Developments (Bauman).- Semantics and Content-based Recommendations (Musto).- Part 2: Special Recommendation Techniques.- Session-based Recommender Systems (lannoch)..- Adversarial Recommender Systems: Attack,Defense, and Advances (Di Nola).- Group Recommender Systems: Beyond Preferance Aggregation (Masthoff).- People-to-People Reciprocal Recommenders (Koprinska).- Natural Language Processing for Recommender Systems (Sar-Shalom).- Design and Evaluation of Cross-domain Recommender Systems (Cremonesi).- Part 3: Value and Impact of Recommender Systems.- Value and Impact of Recommender Systems (Zanker).- Evaluating Recommender Systems (Shani).- Novelty and Diversity in Recommender Systems (Castells).- Multistakeholder Recommender Systems (Burke).- Fairness in Recommender Systems (Ekstrand).- Part 4: Human Computer Interaction.- Beyond Explaining Single Item Recommendations (Tintarev).- Personality and Recommender Systems (Tkalčič).- Individual and Group Decision Making and Recommender Systems (Jameson).- Part 5: Recommender Systems Applications.- Social Recommender Systems (Guy).- Food Recommender Systems (Trattner).- Music Recommendation Systems: Techniques, Use Cases, and Challenges (Schedl).- Multimedia Recommender Systems: Algorithms and Challenges (Deldjoo).- Fashion Recommender Systems (Dokoohaki).