About me

I am an assistant professor at Polytechnic University of Bari (Politecnico di Bari), Italy, affiliated with the Information Systems Laboratory (SisInf Lab), led by Prof. Tommaso di Noia, the head of the group. My research focuses on (i) designing multimodal information-seeking systems (recommender systems, interactive QA and conversational recommender systems), and (ii) designing and evaluating machine learning models to understand their robustness, fairness, generalizability, and interpretability. Recent and ongoing projects consider multimedia recommendation systems, multimodal conversational information-seeking, fairness and biases in recommender systems, adversarial machine learning for the security of recommender systems, and federated learning to maintain user privacy in recommender systems.

I defended my Ph.D. dissertation, with "con lode" (highest distinction in Italy) in July 2018! I have received two bachelor's degrees one a B.Sc. in Electrical Engineering from the University of Guilan, and the other a B.A. degree in English, Linguistics from the University of Gothenburg, Sweden. I completed my M.Sc. in Electrical Engineering at the Chalmers University of Technology, Sweden and obtained a Ph.D. degree in Computer Science from Polytechnic University of Milan (Politecnico di Milano), Italy. During my Ph.D., I was a visiting researcher at Johannes Kepler University (JKU) Linz, Austria for a period of 6 months.

I am an active member of the recommender systems (RS) community and regularly publish/present tutorials at major venues, such as RecSys, SIGIR, WSDM, and top-tier journals such as ACM Computing surveys (CSUR), UMUAI, IEEE TKDE, and ACM TIST. In the year 2020, I had two surveys accepted at ACM CSUR centered on the topics outlined above. Furthermore, I had the honor to contribute to two book chapters at the 3rd Edition of the RS handbook. I have been involved in organizing ACM Recommender System Challenges as part of the RecSys conference through 2017-2020 (as a chair or advisor). I have also organized two tasks at the MediaEval benchmarking event in 2018 and 2019.

NEW: Out tutorial AL4REC "Adversarial Machine Learning for Security of Recommender Systems" presented at ECIR'21 is availiable here. [Short Intro. (by Tommaso)], [Full video], [Slides ], [ELIOT Framework], [AML-RecSys Survey]

NEW: The comprehensive survey on "Multimedia Recommender Systems" accepted to ACM Computing Surveys is now availiable online. Domains studied in this work include: fashion, audio (music, sounds), video (movie, user-generated videos), news, social media, food, e-commerce, tourism, among others: https://dl.acm.org/doi/10.1145/3407190

NEW: The comprehensive survey on "A Survey on Adversarial Recommender Systems: from Attack/Defense Strategies to Generative Adversarial Networks" accepted to ACM Computing Surveys. https://dl.acm.org/doi/10.1145/3439729

NEW: "A Flexible Framework for Evaluating User and Item Fairness in Recommender Systems" accepted to UMUAI, the main journal for personalization research, is now online! It extends our RMSE@RecSys'19 paper in several dimensions: large-scale experiment, theoretical support and extensive literature review! https://lnkd.in/dZB8t-y

NEW: A new dataset "Session-Based Hotel Recommendations Dataset" co-authered with Trivago has been accepted to ACM TIST. The link to dataset is provided within the paper. https://bit.ly/2OcguVm

Current Research Topics

  • Recommender systems and personalization (RecSys)
  • Multimedia Recommender Systems (MM-RecSys)
  • Multimodal conversational Information Seeking (MM-CIS)
  • Fairness in Recommender Systems (Fair-RecSys)
  • Advarsarial Machine Learning in Recommender Systems (Security-RecSys)
  • Federated and Privacy-Aware Recommender Systems (Privacy-RecSys)

Seleted Publications

Academic Services

  • Organizer: ACM RecSys Challenge 2019 (session-based hotel recommendation/Trivago), ACM RecSys Challenge 2017 (Job Recommendation/Xing), MediaEval 2019 (MovieRec and NEWSReel), MediaEval 2018 (MovieRec)
  • PC Member: RecSys'19-21, SIGIR'19-21, ACM MM'19-21, ECIR'19-21, UMAP'19-2121, MMSys'19
  • Journal Reviewer: ACM Computing Surveys, UMUAI, TOIS, Frontiers, Expert Systems with Applications, IEEE Access


  • March-2021 One paper accepted to FLAIR'21, on Multi-Step Adversarial Perturbations against Recommender Systems!
  • Dec-2020 Two papers accepted to ECIR'21, a full paper and a tutroial!
  • Nov-2020 The second literature review in 2020 is accepted to ACM Computing Surveys, this time on AML-RecSys!
  • Nov-2020 Our paper on privacy-by-design recommendation learning using federated learning accepted to ACM SAC'21!
  • July-2020 Honored to contribute two book chapters on the new edition of RS handbook. Topics include MMRS and AML for security of RS.!
  • July-2020 Our paper on recommender systems fairness evaluation is accepted to UMUAI!
  • July-2020 Our tutorial on adversarial machine learning in RecSys is accepted to RecSys'20.
  • July-2020 Our paper session-based hotel recommendations dataset done in collobration with Trivago is accepted at ACM TIST.
  • July-2020 Our paper federated learning is accepted to Italian journal of Intelligenza Artificiale.
  • June-2020 Our comprehensive literature review about recommender systems leveraging multimedia content is accepted to ACM Computing Surveys.
  • May-2020 Our literature review on adversarial machine learning in recommender systems has a preprint version.
  • Jan-2020 One full paper accepted to SIGIR 2020.
  • Dec-2019 One full paper accepted to ESWC 2020.
  • Oct-2019 Our tutorial on adversarial machine learning in RecSys accepted to WSDM'20.
  • Oct-2019 A US patent accepted.
  • July-2019 Our paper on federated learning accepted to AIIA'19.
  • Jan-2019 Our paper accepted to UMUAI.