João Vinagre

608 total citations
24 papers, 261 citations indexed

About

João Vinagre is a scholar working on Information Systems, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, João Vinagre has authored 24 papers receiving a total of 261 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Information Systems, 16 papers in Artificial Intelligence and 8 papers in Management Science and Operations Research. Recurrent topics in João Vinagre's work include Recommender Systems and Techniques (16 papers), Data Stream Mining Techniques (9 papers) and Advanced Bandit Algorithms Research (8 papers). João Vinagre is often cited by papers focused on Recommender Systems and Techniques (16 papers), Data Stream Mining Techniques (9 papers) and Advanced Bandit Algorithms Research (8 papers). João Vinagre collaborates with scholars based in Portugal, Spain and France. João Vinagre's co-authors include Alípio Jorge, João Gama, Paweł J. Matuszyk, Myra Spiliopoulou, Benedita Malheiro, Bruno Veloso, Nuno Moniz, José Paulo Leal, Fabien Gouyon and Marcos Aurélio Domingues and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, Information Fusion and Knowledge and Information Systems.

In The Last Decade

João Vinagre

21 papers receiving 244 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
João Vinagre Portugal	11	179	139	67	57	45	24	261
Diane Hu United States	8	182 1.0×	164 1.2×	57 0.9×	63 1.1×	27 0.6×	16	286
Flavian Vasile United States	8	198 1.1×	192 1.4×	83 1.2×	57 1.0×	26 0.6×	20	290
Fedor Borisyuk United States	6	216 1.2×	131 0.9×	35 0.5×	69 1.2×	24 0.5×	11	297
Xuezhi Cao China	10	241 1.3×	300 2.2×	71 1.1×	84 1.5×	23 0.5×	21	408
Hengliang Luo China	11	215 1.2×	148 1.1×	40 0.6×	44 0.8×	15 0.3×	22	300
Daniel Valcarce Spain	9	194 1.1×	119 0.9×	57 0.9×	51 0.9×	12 0.3×	20	247
Qiang Cui China	8	264 1.5×	169 1.2×	40 0.6×	57 1.0×	38 0.8×	13	343
Aghiles Salah Singapore	10	210 1.2×	181 1.3×	42 0.6×	107 1.9×	16 0.4×	14	305
Miguel A. Rueda-Morales Spain	6	244 1.4×	89 0.6×	36 0.5×	97 1.7×	34 0.8×	7	280

Countries citing papers authored by João Vinagre

Since Specialization

Citations

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

Fields of papers citing papers by João Vinagre

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of João Vinagre

This figure shows the co-authorship network connecting the top 25 collaborators of João Vinagre. A scholar is included among the top collaborators of João Vinagre 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 João Vinagre. João Vinagre is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Eriksson, Maria, et al.. (2025). Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation. Proceedings of the AAAI/ACM Conference on AI Ethics and Society. 8(1). 850–864.

Lopes, Daniela, et al.. (2024). Flow Correlation Attacks on Tor Onion Service Sessions with Sliding Subset Sum. 2 indexed citations

Vinagre, João, et al.. (2023). Towards federated learning: An overview of methods and applications. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 13(2). 10 indexed citations

Vinagre, João, et al.. (2022). ORSUM 2022 - 5th Workshop on Online Recommender Systems and User Modeling. SPIRE - Sciences Po Institutional REpository. 661–662.

Vinagre, João, et al.. (2022). Preface to the special issue on dynamic recommender systems and user models. User Modeling and User-Adapted Interaction. 32(4). 503–507. 1 indexed citations

Vinagre, João, et al.. (2022). Flexible Fine-grained Data Access Management for Hyperledger Fabric. 76–84.

Veloso, Bruno, João Gama, Benedita Malheiro, & João Vinagre. (2021). Hyperparameter self-tuning for data streams. Information Fusion. 76. 75–86. 26 indexed citations

Vinagre, João & Nuno Moniz. (2020). Inteligência Artificial. Revista de Ciência Elementar. 8(4). 11 indexed citations

Vinagre, João, et al.. (2020). ORSUM - Workshop on Online Recommender Systems and User Modeling. SPIRE - Sciences Po Institutional REpository. 619–620. 6 indexed citations

10.

Vinagre, João, et al.. (2019). Incremental Multi-Dimensional Recommender Systems: Co-Factorization vs Tensors. Conference on Recommender Systems. 21–35. 2 indexed citations

11.

Vinagre, João, et al.. (2019). Statistically Robust Evaluation of Stream-Based Recommender Systems. IEEE Transactions on Knowledge and Data Engineering. 33(7). 2971–2982. 13 indexed citations

12.

Vinagre, João, et al.. (2019). A Hybrid Recommender System for Improving Automatic Playlist Continuation. IEEE Transactions on Knowledge and Data Engineering. 1–1. 25 indexed citations

13.

Vinagre, João, et al.. (2018). Incremental Matrix Co-factorization for Recommender Systems with Implicit Feedback. 1413–1418. 11 indexed citations

14.

Vinagre, João, Alípio Jorge, & João Gama. (2018). Online bagging for recommender systems. Expert Systems. 35(4). 9 indexed citations

15.

Vinagre, João, Alípio Jorge, & João Gama. (2015). Collaborative filtering with recency-based negative feedback. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT). 963–965. 11 indexed citations

16.

Matuszyk, Paweł J., João Vinagre, Myra Spiliopoulou, Alípio Jorge, & João Gama. (2015). Forgetting methods for incremental matrix factorization in recommender systems. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT). 947–953. 19 indexed citations

17.

Vinagre, João, Alípio Jorge, & João Gama. (2015). An overview on the exploitation of time in collaborative filtering. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 5(5). 195–215. 27 indexed citations

18.

Domingues, Marcos Aurélio, Fabien Gouyon, Alípio Jorge, et al.. (2012). Combining usage and content in an online music recommendation system for music in the long-tail. 925–930. 10 indexed citations

19.

Domingues, Marcos Aurélio, Fabien Gouyon, Alípio Jorge, et al.. (2012). Combining usage and content in an online recommendation system for music in the Long Tail. International Journal of Multimedia Information Retrieval. 2(1). 3–13. 35 indexed citations

20.

Vinagre, João & Alípio Jorge. (2012). Forgetting mechanisms for scalable collaborative filtering. Journal of the Brazilian Computer Society. 18(4). 271–282. 20 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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