Massimo Quadrana
- Information Systems top 0.5%
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Management Science and Operations Research top 2%
- Signal Processing top 5%
- Co-authors
- Paolo CremonesiDietmar JannachBalázs HidasiDomonkos TikkAlexandros KaratzoglouYashar DeldjooMehdi ElahiFranca Garzotto
- Topics
- Recommender Systems and Techniques (16 papers)Advanced Bandit Algorithms Research (7 papers)Music and Audio Processing (6 papers)
In The Last Decade
Massimo Quadrana
22 papers receiving 990 citations
Hit Papers
Peers
Comparison fields: 5 of 75
- Information Systems 821
- Artificial Intelligence 537
- Computer Vision and Pattern Recognition 353
- Management Science and Operations Research 238
- Signal Processing 150
Countries citing papers authored by Massimo Quadrana
This map shows the geographic impact of Massimo Quadrana's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Massimo Quadrana with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Massimo Quadrana more than expected).
Fields of papers citing papers by Massimo Quadrana
This network shows the impact of papers produced by Massimo Quadrana. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Massimo Quadrana. The network helps show where Massimo Quadrana may publish in the future.
Co-authorship network of co-authors of Massimo Quadrana
This figure shows the co-authorship network connecting the top 25 collaborators of Massimo Quadrana. A scholar is included among the top collaborators of Massimo Quadrana based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Massimo Quadrana. Massimo Quadrana is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 3 | |
| 3 | 0 | |
| 4 | 5 | |
| 5 | 15 | |
| 6 | 5 | |
| 7 | Sequence-Aware Recommender Systemsbreakdown → | 273 |
| 8 | 47 | |
| 9 | The importance of song context in music playlists: Enabling recommendations in the long tail | 1 |
| 10 | The Importance of Song Context in Music Playlists. | 7 |
| 11 | 7 | |
| 12 | 5 | |
| 13 | 4 | |
| 14 | 148 | |
| 15 | Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendationsbreakdown → | 341 |
| 16 | 10 | |
| 17 | 30Music listening and playlists dataset | 27 |
| 18 | 10 | |
| 19 | 25 | |
| 20 | 12 |
About Massimo Quadrana
Massimo Quadrana is a scholar working on Information Systems, Signal Processing and Management Science and Operations Research, having authored 24 papers that have together received 1.0k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (16 papers), Advanced Bandit Algorithms Research (7 papers) and Music and Audio Processing (6 papers). The work is most often cited by research in Information Systems (821 citations), Management Science and Operations Research (238 citations) and Computer Vision and Pattern Recognition (353 citations). Massimo Quadrana has collaborated with scholars based in Italy, Austria and Spain. Frequent co-authors include Paolo Cremonesi, Dietmar Jannach, Balázs Hidasi, Domonkos Tikk, Alexandros Karatzoglou, Yashar Deldjoo, Mehdi Elahi, Franca Garzotto, Pietro Piazzolla and Roberto Pagano. Their work appears in journals such as IEEE Access, ACM Computing Surveys and AI Communications.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.