Marco Polignano

1.1k total citations
52 papers, 552 citations indexed

About

Marco Polignano is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Marco Polignano has authored 52 papers receiving a total of 552 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Artificial Intelligence, 23 papers in Information Systems and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Marco Polignano's work include Recommender Systems and Techniques (20 papers), Topic Modeling (16 papers) and Sentiment Analysis and Opinion Mining (9 papers). Marco Polignano is often cited by papers focused on Recommender Systems and Techniques (20 papers), Topic Modeling (16 papers) and Sentiment Analysis and Opinion Mining (9 papers). Marco Polignano collaborates with scholars based in Italy, Netherlands and Austria. Marco Polignano's co-authors include Giovanni Semeraro, Marco de Gemmis, Pierpaolo Basile, Cataldo Musto, Valerio Basile, Pasquale Lops, Fedelucio Narducci, Viviana Patti, Cristina Bosco and Marco Antonio Stranisci and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Expert Systems with Applications.

In The Last Decade

Marco Polignano

43 papers receiving 516 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Marco Polignano Italy 12 415 184 54 51 50 52 552
Puneet Mathur India 12 404 1.0× 104 0.6× 24 0.4× 36 0.7× 46 0.9× 47 570
Manoj Kumar Chinnakotla India 10 461 1.1× 120 0.7× 63 1.2× 34 0.7× 34 0.7× 23 553
Senja Pollak Slovenia 13 384 0.9× 77 0.4× 36 0.7× 68 1.3× 30 0.6× 73 510
Pei-Yun Hsueh United States 10 275 0.7× 66 0.4× 30 0.6× 32 0.6× 38 0.8× 36 453
Sheng-yi Kong Taiwan 7 721 1.7× 149 0.8× 19 0.4× 74 1.5× 70 1.4× 12 902
Mudasir Ahmad Wani Saudi Arabia 11 237 0.6× 110 0.6× 22 0.4× 96 1.9× 42 0.8× 34 449
Guangchen Ruan United States 7 131 0.3× 104 0.6× 38 0.7× 108 2.1× 31 0.6× 22 400
Thiago Alexandre Salgueiro Pardo Brazil 18 910 2.2× 198 1.1× 27 0.5× 128 2.5× 27 0.5× 122 1.1k
Kathy McKeown United States 14 518 1.2× 119 0.6× 31 0.6× 54 1.1× 16 0.3× 26 632
Suma Bhat United States 15 368 0.9× 81 0.4× 49 0.9× 24 0.5× 11 0.2× 65 549

Countries citing papers authored by Marco Polignano

Since Specialization
Citations

This map shows the geographic impact of Marco Polignano'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 Marco Polignano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Polignano more than expected).

Fields of papers citing papers by Marco Polignano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marco Polignano. 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 Marco Polignano. The network helps show where Marco Polignano may publish in the future.

Co-authorship network of co-authors of Marco Polignano

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Polignano. A scholar is included among the top collaborators of Marco Polignano 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 Marco Polignano. Marco Polignano 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.
2.
Jannach, Dietmar, et al.. (2025). RecSys Challenge 2025: Universal Behavioral Profiles for Recommender Systems. Research Publications (Maastricht University). 1389–1393.
3.
Musto, Cataldo, Marco Polignano, Amon Rapp, Giovanni Semeraro, & Jürgen Ziegler. (2025). 7th Workshop on Explainable User Models and Personalised Systems (ExUM 2025). CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 174–176. 1 indexed citations
4.
Polignano, Marco, et al.. (2025). Comparing Human Pose Estimation through deep learning approaches: An overview. Computer Vision and Image Understanding. 252. 104297–104297. 3 indexed citations
5.
Lops, Pasquale, et al.. (2024). Reproducibility of LLM-based Recommender Systems: the Case Study of P5 Paradigm. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 116–125. 1 indexed citations
6.
Basile, Pierpaolo, et al.. (2024). Adapting BLOOM to a new language: A case study for the Italian. SHILAP Revista de lepidopterología. 10(1).
7.
Polignano, Marco, et al.. (2024). EB-NeRD a large-scale dataset for news recommendation. arXiv (Cornell University). 1–11. 5 indexed citations
8.
Lops, Pasquale, et al.. (2023). Reproducibility Analysis of Recommender Systems relying on Visual Features: traps, pitfalls, and countermeasures. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 554–564. 1 indexed citations
9.
Lops, Pasquale, Cataldo Musto, & Marco Polignano. (2023). Accountable Knowledge-aware Recommender Systems. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 306–308. 1 indexed citations
10.
Polignano, Marco, Pasquale Lops, Marco de Gemmis, & Giovanni Semeraro. (2022). HELENA: An intelligent digital assistant based on a Lifelong Health User Model. Information Processing & Management. 60(1). 103124–103124. 2 indexed citations
11.
Bonnici, Vincenzo, et al.. (2022). Covid19/IT the digital side of Covid19: A picture from Italy with clustering and taxonomy. PLoS ONE. 17(6). e0269687–e0269687. 4 indexed citations
12.
Polignano, Marco, et al.. (2021). Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images. Information. 12(8). 321–321. 1 indexed citations
13.
Polignano, Marco, Marco de Gemmis, & Giovanni Semeraro. (2021). Comparing Transformer-based NER approaches for analysing textual medical diagnoses. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 2936. 818–833. 3 indexed citations
14.
Polignano, Marco, et al.. (2020). HealthAssistantBot: A Personal Health Assistant for the Italian Language. IEEE Access. 8. 107479–107497. 40 indexed citations
15.
Musto, Cataldo, Marco Polignano, Giovanni Semeraro, Marco de Gemmis, & Pasquale Lops. (2020). Myrror: a platform for holistic user modeling. User Modeling and User-Adapted Interaction. 30(3). 477–511. 12 indexed citations
16.
Polignano, Marco, et al.. (2020). A study of Machine Learning models for Clinical Coding of Medical Reports at CodiEsp 2020.. CLEF (Working Notes). 4 indexed citations
17.
Lai, Mirko, Valerio Basile, Fabio Poletto, et al.. (2020). “Contro L’Odio”: A Platform for Detecting, Monitoring and Visualizing Hate Speech against Immigrants in Italian Social Media. SHILAP Revista de lepidopterología. 6(1). 77–97. 7 indexed citations
18.
Polignano, Marco, Valerio Basile, Pierpaolo Basile, Marco de Gemmis, & Giovanni Semeraro. (2019). AlBERTo: Modeling Italian Social Media Language with BERT. SHILAP Revista de lepidopterología. 5(2). 11–31. 13 indexed citations
19.
Polignano, Marco, et al.. (2019). Identification Of Bot Accounts In Twitter Using 2D CNNs On User-generated Contents.. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 4 indexed citations
20.
Polignano, Marco, Pierpaolo Basile, Marco de Gemmis, & Giovanni Semeraro. (2018). An Emotion-driven Approach for Aspect-based Opinion Mining.. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 2140. 2 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.

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