About me

Senior Applied Scientist at Amazon Music specializing in machine learning and recommender systems, with a strong focus on data-driven optimization, causal inference, and large-scale experimentation. Passionate about bridging research and production to deliver measurable business impact through intelligent systems.

Resume

Experience

  1. Senior Applied Scientist III - Amazon Music

    April 2025 — Present

  2. Applied Scientist II - Amazon Music

    January 2022 — April 2025

  3. Applied Scientist I - Amazon Music

    January 2020 — December 2021

  4. Applied Scientist Intern - Amazon Music

    April 2019 - August 2019

  5. Teaching Assistant - Polytechnic University of Bari

    2016 - 2017

    Lecturer for the "Algorithms and Data Structure in Java" class. - B.S. in Computer Science.

Education

  1. PhD, Computer Science & Engineering - Polytechnic University of Bari

    2016 — 2019

    During the Ph.D., I focused my research on Deep Learning applied to Recommender Systems. In particular, I investigated on Neural Networks interpretability and explainable models in recommendation scenarios.

  2. M.S. in Computer Science - Polytechnic University of Bari

    2013 — 2016

    Developed a Recommender System in the movie domain.

  3. B.S. in Computer Science - Polytechnic University of Bari

    2009 — 2013

Research

Conferences

  1. A Unified Recommendation Model for Features Summarization

    Vito Bellini, Zhan Shi, Huseyin Yurtseven, and Emanuele Coviello

    3rd Music Recommender Systems Workshop (MuRS 2025) co-located with the 19th ACM Conference on Recommender Systems (RecSys 2025), September 2025.

  2. Modeling position bias ranking for streaming media services

    Matteo Ruffini, Vito Bellini, Alexander Buchholz, Giuseppe Di Benedetto, and Yannik Stein

    The Web Conference 2022

  3. Fair effect attribution in parallel online experiments

    Alexander Buchholz, Vito Bellini, Giuseppe Di Benedetto, Yannik Stein, Matteo Ruffini, and Fabian Moerchen

    The Web Conference 2022

  4. Guapp: A conversational agent for job recommendation for the italian public administration

    Vito Bellini, Giovanni Maria Biancofiore, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci, and Claudio Pomo

    IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)

  5. Knowledge-aware autoencoders for explainable recommender system

    Vito Bellini, Angelo Schiavone, Tommaso Di Noia, Azzurra Ragone, and Eugenio Di Sciascio

    Deep Learning for Recommender Systems Workshop (RecSys)

  6. Reflective internet of things middleware-enabled a predictive real-time waste monitoring system

    Vito Bellini, Tommaso Di Noia, Marina Mongiello, Francesco Nocera, Angelo Parchitelli, and Eugenio Di Sciascio

    International Conference Web Engineering (ICWE)

  7. Auto-encoding user ratings via knowledge graphs in recommendation scenarios

    Vito W Anelli, Vito Bellini, Tommaso Di Noia, Wanda La Bruna, Paolo Tomeo, and Eugenio Di Sciascio

    Deep Learning for Recommender Systems Workshop (RecSys)

  8. An analysis on time-and session-aware diversification in recommender systems

    Vito Bellini, Vito Walter Anelli, Tommaso Di Noia, and Eugenio Di Sciascio

    Conference on User Modeling, Adaptation and Personalization (UMAP)

  9. Querying deep web data sources as linked data

    Vito W Anelli, Vito Bellini, Andrea Calí, Giuseppe De Santis, Tommaso Di Noia, and Eugenio Di Sciascio

    International Conference on Web Intelligence, Mining and Semantics

Journals

  1. A qualitative analysis of knowledge graphs in recommendation scenarios through semantics-aware autoencoders

    Vito Bellini, Eugenio Di Sciascio, Francesco Maria Donini, Claudio Pomo, Azzurra Ragone, and Angelo Schiavone

    Journal of Intelligent Information Systems, pages 1–21. Springer, 2024

  2. Knowledge-aware interpretable recommender systems

    Vito Walter Anelli, Vito Bellini, Tommaso Di Noia, and Eugenio Di Sciascio

    Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges, pages 101–124. IOS Press, 2020.

  3. Semantics-aware autoencoder

    Vito Bellini, Tommaso Di Noia, Eugenio Di Sciascio, and Angelo Schiavone

    IEEE Access, volume 7, pages 166122–166137. IEEE, 2019.

Patents

  1. Techniques for content selection in seasonal environment

    Giuseppe Di Benedetto, Vito Bellini, and Giovanni Zappella

    US Patent 11,586,965