Roberto Pagano received his BSc in Computer Engineering from Università degli Studi di Palermo in 2010. Within a double degree program, he received the MSc cum laude in Computer Engineering from the Politecnico di Torino and from the Politecnico di Milano in 2012. He is currently a PhD student in Information Technology at Politecnico di Milano, Department of Electronics, Information and Bioengineering (DEIB), under the supervision of Professor Paolo Cremonesi.
His research interests include Context Aware Recommender Systems, Machine Learning, Reinforcement Learning, Optimization, Time Series Analysis, Scalability, Parallel Programming, Cloud computing.
Roberto Pagano worked as intern at British Institutes and gave lectures at Politecnico di Milano and Cefriel.
Yashar Deldjoo is a PhD student in information technology under supervision of Prof. Paolo Cremonesi at Polytechnic university of Milan, Italy. His research interests include the field of multimedia information retrieval with focus on multimedia content analysis for Recommender Systems.
Mehdi Elahi received M.Sc. degree in Electrical Engineering (Sweden), and Ph.D. degree in Computer Science (Italy), under the supervision of Prof. Francesco Ricci. During the course of the Ph.D., he has researched on Recommender Systems (RSs), mainly focused on the cold start problem. He has designed, developed, and evaluated (offline/online) several personalized techniques for Active Learning in RSs that resulted in significant improvement over the state-of-the-art solutions (Ph.D. Thesis, and Presentation). These techniques were integrated in a mobile context-aware RS for tourism (South Tyrol Suggests), and a health-aware food RS (ChefPad).
As a result of his graduate research work, Mehdi Elahi served as a primary author or co-author on several publications in AI, ML, IR, HCI, UM, and UA related conferences (list of publications and downloadable PDFs). He was also given the opportunity to publish his research findings in the form of five key publications, i.e., a book chapter in the second edition of Recommender Systems Handbook, an article in a top ACM journal (ACM-TIST), which is ranked #4 among 719 journals in Artificial Intelligence, an article in a top Springer journal (UMUAI), ranked #12 among 1000 journals in Computer Science, a survey article in Computer Science Review journal, which is ranked #27 among 145 journals in Theoretical Computer Science, and an article in the journal of Data Semantics (JODS).
He also had the opportunity to work on several research projects, during his Ph.D. and Postdoc, comprising SUSDEM (Supporting Sequential Item Selection with Recommendation), SIPAI (Proactive Information Access Systems), and STS (South Tyrol Suggests). Moreover, he has been actively involved in preparation of sets of datasets:
- STS dataset: [download, description, related app] (user personality + context + rating on POIs)
- Mise-en-scene dataset: [download, description, related app] (colors + motions +lights in movies)
- Mise-en-scene MPEG7 dataset: [download, description, related app] (mpeg-7 colors+texture features in movies)
- PoliMovie dataset: [download , description] (user ratings on directors + casts + genre + prod_year of movies)
He is currently working at Politecnico di Milano as a Postdoc researcher, under advisory of Prof. Paolo Cremonesi, and in collaboration with Prof. Franca Garzotto, researching on Recommender Systems, mainly focused on Mise-en-scène Project, and coordinating the work of four Ph.D. students. In addition to the research experience obtained during his doctoral work, he has co-invented a US-patent, submitted recently. He served as an organizer of the RecSys challenge 2017, as well as, the proceeding chair of the conference. He has worked as a laboratory instructor for both of the bachelor and master level courses at Libera Universita di Bolzano (1, 2) and Politecnico di Milano (1). He has also been invited to review submissions to various journals, listed below:
- ACM Transactions on Interactive Intelligent Systems (TIIS)
- ACM Transactions on the Web (TWEB)
- ACM Journal on Computing and Cultural Heritage (JOCCH)
- Springer journal of User Modeling and User-Adapted Interaction (UMUAI)
- Springer journal of Multimedia Tools and Applications (MTAP)
- Elsevier Expert Systems with Applications
- Elsevier Information Sciences
- Elsevier Information Processing and Management
- Elsevier Journal of Systems and Software
- IEEE Transactions on Knowledge and Data Engineering (TKDE)
- IEEE Intelligent Systems
- Journal of Interaction Studies
- Journal of Interaction Design and Architecture (IxD&A)
MELAHI at KTH dot SE or
MEHDI.ELAHI at POLIMI dot IT
Mona Nasery is a Post- M. Sc Researcher jointly atDepartment of Electronic, Information and Bioengineering ofPolitecnico di Milano and EIT-Digital since November 2013. She obtained her M.Sc. in Computer Science and Engineering in Politecnico di Milano on October 2013. Her main research theme is evaluating performance of recommender systems. Some of her main research interests are:
- Cross-domain recommendations
- Attribute-based recommender systems
- Design and develop web applications for recommender systems
- Databases and datamining
- Decision making behavior of users in Recommender systems
Currently working as data scientist, coordinating the data analysis and research activities to support the development of a B2B recommender system for digital media, with a 10-year experience in the field, including both academic research and its application to production environments.
I obtained a Ph.D. in Computer Engineering and worked for about 2 years as post-doc at Politecnico di Milano (Milan, Italy), where I was involved both in research and in teaching activities.
As a teacher, I taught to B.sc. and M.sc. students, both as course owner and as assistant professor, also supervising students in projects and theses.
I am still involved in research activities, being the leader of one of the work packages – of a three-year FP7 European project (namely CrowdRec) – dedicated to the development of recommendation algorithms. I published several scientific papers, mainly in the area of recommender systems, presenting the main outcomes in international conferences. I am involved and well-recognized in the recommender systems community, being part of the program committee of the main scientific conference on recommender systems (ACM RecSys), organizer of several workshops (e.g., RecSysTV workshop, CrowdRec workshop) and of the ACM RecSys challenge 2014, and presenting tutorials.