I am a PhD student in Information Technology at Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), under the supervision of prof. Paolo Cremonesi. My research interests include sequential recommender systems, deep-learning and large-scale machine learning.
I'm currently a Music Science intern at Pandora Media Inc. (Oakland, CA, USA). I'm working with Tao Ye, Erik Schmidt and Puya-Hossein Vahabi on Deep Learning algorithms for sequence classification.
Jun 2016I was a Research Intern at Telefonica I+D (Barcelona, Spain). I worked with Alexandros Karatzoglou and Balázs Hidasi (Gravity R&D) on Recurrent Neural Networks for Session Based Recommendation.
Sept 2015 - May 2016I started my PhD in Information Technology at the Polictecnico di Milano, Milan, Italy. I'll be working on Sequential Recommender Systems.
Sep 2013 - OngoingI received the M.Sc. with laude in Computer science at Politecnico di Milano (Double degree with the Univirsitat Politecnica de Catalunya, Barcelona, Spain). Thesis: E-Tourism Recommender Systems - Advisor: Prof. Paolo Cremonesi
Sep 2013I received the M.Sc. in Computer science at Univirsitat Politecnica de Catalunya, Barcelona, Spain. Thesis: Methods for frequent pattern mining in data streams within the MOA system - Advisor: Prof. Ricard Gavaldá
Sep 2013I received the B.Sc. in Computer science at Politecnico di Milano. Thesis: Integration and evaluation of featuretracking algorithms within a visual SLAM system - Advisor: Prof. Matteo Matteucci
Sep 2010That is an excerpt of my recent pubblications. The full list is available on here.
In ConferencesMassimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi and Paolo Cremonesi. 2017. Personalizing Session-based Recommendation with Hierarchical Recurrent Neural Networks. ACM Recsys 2017 [To appear] PDF CODE
Leonardo Cella, Stefano Cereda, Massimo Quadrana and Paolo Cremonesi. 2017. Deriving Item Features Relevance from Past User Interactions. ACM UMAP 2017 [To appear] PDF
Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou and Domonkos Tikk. 2016. Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations. ACM Recsys 2016 PDF
Roberto Turrin, Massimo Quadrana, Roberto Pagano, Paolo Cremonesi and Andrea Condorelli. “30Music listening and playlists dataset” ACM Recsys 2015 PDF
Yashar Deldjoo, Mehdi Elahi, Massimo Quadrana, Paolo Cremonesi, and Franca Garzotto. 2015. Toward Effective Movie Recommendations Based on Mise-en-Scène Film Styles. In Proceedings of the 11th Biannual Conference on Italian SIGCHI Chapter (CHItaly 2015). ACM, New York, NY, USA, 162-165 PDF
Paolo Cremonesi and Massimo Quadrana. 2014. Cross-domain Recommendations without Overlapping Data: Myth or Reality?. In Proceedings of the 8th ACM Conference on Recommender systems (RecSys '14). ACM, New York, NY, USA, 297-300. PDF
Paolo Cremonesi, Franca Garzotto, Roberto Pagano, and Massimo Quadrana. 2014. Recommending without short head. In Proceedings of the companion publication of the 23rd international conference on World wide web companion (WWW Companion '14). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 245-246. PDF
Paolo Cremonesi, Franca Garzotto, and Massimo Quadrana. 2013. Evaluating top-n recommendations "when the best are gone". In Proceedings of the 7th ACM conference on Recommender systems (RecSys '13). ACM, New York, NY, USA, 339-342. PDF
In JournalsMassimo Quadrana, Paolo Cremonesi and Dietmar Jannach. Sequence-aware Recommender Systems ACM Computing Surveys (preprint). PDF
Massimo Quadrana, Albert Bifet, and Ricard Gavaldà. An efficient closed frequent itemset miner for the MOA stream mining system. AI Communications28.1 (2015): 143-158. PDF
In WorkshopsAndrea Condorelli, Paolo Cremonesi, Roberto Pagano, Massimo Quadrana and Roberto Turrin. “Large Scale Music Recommendation”. 3rd Workshop on Large-Scale Recommender Systems, ACM Recsys 2015 PDF