Massimo Quadrana

1.7k total citations · 2 hit papers
24 papers, 1.0k citations indexed

About

Massimo Quadrana is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Massimo Quadrana has authored 24 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Information Systems, 12 papers in Artificial Intelligence and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Massimo Quadrana's work include Recommender Systems and Techniques (16 papers), Advanced Bandit Algorithms Research (7 papers) and Music and Audio Processing (6 papers). Massimo Quadrana is often cited by papers focused on Recommender Systems and Techniques (16 papers), Advanced Bandit Algorithms Research (7 papers) and Music and Audio Processing (6 papers). Massimo Quadrana collaborates with scholars based in Italy, Austria and Spain. Massimo Quadrana's co-authors include Paolo Cremonesi, Dietmar Jannach, Balázs Hidasi, Domonkos Tikk, Alexandros Karatzoglou, Yashar Deldjoo, Mehdi Elahi, Franca Garzotto, Pietro Piazzolla and Roberto Pagano and has published in prestigious journals such as IEEE Access, ACM Computing Surveys and AI Communications.

In The Last Decade

Massimo Quadrana

22 papers receiving 990 citations

Hit Papers

Parallel Recurrent Neural Network Architectures for Featu... 2016 2026 2019 2022 2016 2018 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations
    2016 341 citations Balázs Hidasi, Massimo Quadrana et al. profile →
  2. Sequence-Aware Recommender Systems
    2018 273 citations Massimo Quadrana, Paolo Cremonesi et al. ACM Computing Surveys profile →

Peers

Massimo Quadrana
Saúl Vargas Spain
YoungOk Kwon United States
Jinoh Oh South Korea
Malte Ludewig Germany
Azin Ashkan Canada
Nathan N. Liu Hong Kong
Fajie Yuan China
Pipei Huang China
Alper Bilge Türkiye
David Vallet Spain
Saúl Vargas Spain
Massimo Quadrana
Citations per year, relative to Massimo Quadrana Massimo Quadrana (= 1×) peers Saúl Vargas

Countries citing papers authored by Massimo Quadrana

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Ferraro, Andrés, et al.. (2023). MuRS: Music Recommender Systems Workshop. 1227–1230. 1 indexed citations
2.
Dacrema, Maurizio Ferrari, et al.. (2022). From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain. IEEE Access. 10. 14779–14796. 3 indexed citations
3.
Oramas, Sergio, Massimo Quadrana, & Fabien Gouyon. (2021). Bootstrapping a Music Voice Assistant with Weak Supervision. INFM-OAR (INFN Catania). 49–55.
4.
Quadrana, Massimo, Dietmar Jannach, & Paolo Cremonesi. (2019). Tutorial: Sequence-Aware Recommender Systems. 1316–1316. 5 indexed citations
5.
Vall, Andreu, Massimo Quadrana, Markus Schedl, & Gerhard Widmer. (2019). Order, context and popularity bias in next-song recommendations. International Journal of Multimedia Information Retrieval. 8(2). 101–113. 15 indexed citations
6.
Quadrana, Massimo & Paolo Cremonesi. (2018). Sequence-aware recommendation. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 539–540. 5 indexed citations
7.
Quadrana, Massimo, Paolo Cremonesi, & Dietmar Jannach. (2018). Sequence-Aware Recommender Systems. ACM Computing Surveys. 51(4). 1–36. 273 indexed citations breakdown →
8.
Deldjoo, Yashar, Mehdi Elahi, Massimo Quadrana, & Paolo Cremonesi. (2018). Using visual features based on MPEG-7 and deep learning for movie recommendation. International Journal of Multimedia Information Retrieval. 7(4). 207–219. 47 indexed citations
9.
Vall, Andreu, Markus Schedl, Gerhard Widmer, Massimo Quadrana, & Paolo Cremonesi. (2017). The importance of song context in music playlists: Enabling recommendations in the long tail. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1905. 1–2. 1 indexed citations
10.
Vall, Andreu, Massimo Quadrana, Markus Schedl, Gerhard Widmer, & Paolo Cremonesi. (2017). The Importance of Song Context in Music Playlists.. Conference on Recommender Systems. 7 indexed citations
11.
Deldjoo, Yashar, Paolo Cremonesi, Markus Schedl, & Massimo Quadrana. (2017). The effect of different video summarization models on the quality of video recommendation based on low-level visual features. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–6. 7 indexed citations
12.
Quadrana, Massimo, et al.. (2017). Deriving Item Features Relevance from Past User Interactions. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 275–279. 5 indexed citations
13.
Cremonesi, Paolo, et al.. (2016). Multi-stack ensemble for job recommendation. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–4. 4 indexed citations
14.
Deldjoo, Yashar, Mehdi Elahi, Paolo Cremonesi, et al.. (2016). Content-Based Video Recommendation System Based on Stylistic Visual Features. BOA (University of Milano-Bicocca). 5(2). 99–113. 148 indexed citations
15.
Hidasi, Balázs, Massimo Quadrana, Alexandros Karatzoglou, & Domonkos Tikk. (2016). Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations. 241–248. 341 indexed citations breakdown →
16.
Quadrana, Massimo, Albert Bifet, & Ricard Gavaldà. (2015). An efficient closed frequent itemset miner for the MOA stream mining system. AI Communications. 28(1). 143–158. 10 indexed citations
17.
Turrin, Roberto, et al.. (2015). 30Music listening and playlists dataset. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1441. 27 indexed citations
18.
Deldjoo, Yashar, Mehdi Elahi, Massimo Quadrana, Paolo Cremonesi, & Franca Garzotto. (2015). Toward Effective Movie Recommendations Based on Mise-en-Scène Film Styles. View. 162–165. 10 indexed citations
19.
Cremonesi, Paolo & Massimo Quadrana. (2014). Cross-domain recommendations without overlapping data. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 297–300. 25 indexed citations
20.
Cremonesi, Paolo, Franca Garzotto, Roberto Pagano, & Massimo Quadrana. (2014). Recommending without short head. BOA (University of Milano-Bicocca). 245–246. 12 indexed citations

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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Saúl Vargas Breakdown of academic impact, for papers by YoungOk Kwon Breakdown of academic impact, for papers by Jinoh Oh Breakdown of academic impact, for papers by Malte Ludewig Breakdown of academic impact, for papers by Azin Ashkan Breakdown of academic impact, for papers by Nathan N. Liu Breakdown of academic impact, for papers by Fajie Yuan Breakdown of academic impact, for papers by Pipei Huang Breakdown of academic impact, for papers by Alper Bilge Breakdown of academic impact, for papers by David Vallet
Rankless by CCL
2026