Nava Tintarev

3.0k total citations
49 papers, 1.3k citations indexed

About

Nava Tintarev is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Nava Tintarev has authored 49 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Information Systems, 20 papers in Artificial Intelligence and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Nava Tintarev's work include Recommender Systems and Techniques (17 papers), Multi-Agent Systems and Negotiation (5 papers) and Consumer Market Behavior and Pricing (5 papers). Nava Tintarev is often cited by papers focused on Recommender Systems and Techniques (17 papers), Multi-Agent Systems and Negotiation (5 papers) and Consumer Market Behavior and Pricing (5 papers). Nava Tintarev collaborates with scholars based in United Kingdom, Netherlands and United States. Nava Tintarev's co-authors include Judith Masthoff, Josep M. Pujol, Nuria Oliver, Xavier Amatriain, Yucheng Jin, Katrien Verbert, Tim Draws, Alexander Felfernig, Ehud Reiter and Shahin Rostami and has published in prestigious journals such as Communications of the ACM, IEEE Transactions on Software Engineering and Frontiers in Psychology.

In The Last Decade

Nava Tintarev

48 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nava Tintarev United Kingdom 17 697 688 242 172 167 49 1.3k
Ludovico Boratto Italy 21 700 1.0× 458 0.7× 189 0.8× 180 1.0× 152 0.9× 117 1.1k
Shilad Sen United States 19 848 1.2× 614 0.9× 291 1.2× 130 0.8× 275 1.6× 38 1.5k
Rishabh Mehrotra United Kingdom 14 548 0.8× 579 0.8× 132 0.5× 217 1.3× 165 1.0× 53 1.1k
Michael D. Ekstrand United States 21 1.3k 1.9× 749 1.1× 350 1.4× 399 2.3× 257 1.5× 61 1.9k
Denis Parra Chile 22 760 1.1× 550 0.8× 429 1.8× 120 0.7× 236 1.4× 93 1.5k
John O’Donovan United States 24 1.3k 1.8× 924 1.3× 560 2.3× 218 1.3× 497 3.0× 62 2.3k
Kirsten Swearingen United States 9 962 1.4× 708 1.0× 445 1.8× 138 0.8× 274 1.6× 11 1.7k
Stefano Mizzaro Italy 20 946 1.4× 767 1.1× 184 0.8× 187 1.1× 188 1.1× 102 1.6k
Mehdi Elahi Italy 17 731 1.0× 401 0.6× 416 1.7× 173 1.0× 144 0.9× 58 1.2k
Al Mamunur Rashid United States 14 1.1k 1.6× 613 0.9× 292 1.2× 258 1.5× 380 2.3× 18 1.9k

Countries citing papers authored by Nava Tintarev

Since Specialization
Citations

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

Fields of papers citing papers by Nava Tintarev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nava Tintarev

This figure shows the co-authorship network connecting the top 25 collaborators of Nava Tintarev. A scholar is included among the top collaborators of Nava Tintarev 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 Nava Tintarev. Nava Tintarev 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.
Draws, Tim, et al.. (2023). Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability. Research Publications (Maastricht University). 1–21. 34 indexed citations
2.
Spano, Lucio Davide, et al.. (2023). Supporting High-Uncertainty Decisions through AI and Logic-Style Explanations. Research Publications (Maastricht University). 251–263. 12 indexed citations
3.
Bianchi, Federico, et al.. (2022). “It’s Not Just Hate”: A Multi-Dimensional Perspective on Detecting Harmful Speech Online. 8093–8099. 8 indexed citations
4.
Burke, Robin, Michael D. Ekstrand, Nava Tintarev, & Julita Vassileva. (2021). Preface to the special issue on fair, accountable, and transparent recommender systems. User Modeling and User-Adapted Interaction. 31(3). 371–375. 5 indexed citations
5.
Jin, Yucheng, Nyi Nyi Htun, Nava Tintarev, & Katrien Verbert. (2019). ContextPlay. Research Repository (Delft University of Technology). 294–302. 22 indexed citations
6.
Harambam, Jaron, et al.. (2019). SIREN. UvA-DARE (University of Amsterdam). 150–159. 36 indexed citations
7.
Smith, Kirsten A., et al.. (2019). A methodology for creating and validating psychological stories for conveying and measuring psychological traits. User Modeling and User-Adapted Interaction. 29(3). 573–618. 12 indexed citations
8.
Lu, Feng & Nava Tintarev. (2018). A Diversity Adjusting Strategy with Personality for Music Recommendation. Research Repository (Delft University of Technology). 7–14. 14 indexed citations
9.
Tintarev, Nava, et al.. (2018). TourExplain : A Crowdsourcing Pipeline for Generating Explanations for Groups of Tourists. Data Archiving and Networked Services (DANS). 33–36. 4 indexed citations
10.
Smith, Kirsten A., Judith Masthoff, & Nava Tintarev. (2016). Expressing Emotions as Emoticons for Online Intelligent Agents. Electronic workshops in computing. 2 indexed citations
11.
Tintarev, Nava & Judith Masthoff. (2016). Effects of Individual Differences in Working Memory on Plan Presentational Choices. Frontiers in Psychology. 7. 1793–1793. 9 indexed citations
12.
Tintarev, Nava, et al.. (2015). Benefits and risks of emphasis adaptation in study workflows. Bournemouth University Research Online (Bournemouth University). 1 indexed citations
13.
Tintarev, Nava, et al.. (2015). Inspection Mechanisms for Community-based Content Discovery in Microblogs.. Conference on Recommender Systems. 21–28. 6 indexed citations
14.
Tintarev, Nava, John O’Donovan, Peter Brusilovsky, et al.. (2014). RecSys'14 joint workshop on interfaces and human decision making for recommender systems. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 383–384. 4 indexed citations
15.
Tintarev, Nava, et al.. (2014). Demo. Aberdeen University Research Archive (Aberdeen University). 29–32. 5 indexed citations
16.
Tintarev, Nava, Yolanda Melero, Somayajulu Sripada, et al.. (2012). MinkApp: Generating Spatio-temporal Summaries for Nature Conservation Volunteers. Open Access Institutional Repository at Robert Gordon University (Robert Gordon University). 17–21. 4 indexed citations
17.
Tintarev, Nava & Judith Masthoff. (2012). Evaluating the effectiveness of explanations for recommender systems. User Modeling and User-Adapted Interaction. 22(4-5). 399–439. 220 indexed citations
18.
Black, Rolf, et al.. (2010). Using NLG and Sensors to Support Personal Narrative for Children with Complex Communication Needs. Discovery Research Portal (University of Dundee). 1–9. 20 indexed citations
19.
Tintarev, Nava & Judith Masthoff. (2009). Evaluating recommender explanations: Problems experienced and lessons learned for the evaluation of adaptive systems. 54–63. 5 indexed citations
20.
Tintarev, Nava. (2007). Explanations of recommendations. 203–206. 72 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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