Dr. Nava Tintarev is an Assistant Professor and Delft Technology Fellow in the Web Information Systems group
, Faculty of Electrical Engineering, Mathematics and Computer Science at TU Delft. She studies how to best select, present, and adapt the presentation of information (using both natural language generation, and visualizations) in artificial advice giving systems.
The ENSURE (ExplaiNing SeqUences in REcommendations)
project looks at ways of improving the transparency and decision support for recommender systems (like Amazon and Spotify), in recommendation scenarios that contain both surprising recommendations and trade-offs. She is also leading a smaller project looking at issues relating to critical thinking in higher education: SuSPECT: Scaffolding Student PErspectives for Critical Thinking
. She is interested in tackling issues regarding ethics in big data, algorithmic transparency, diversity, and filter bubbles.
Nava was previously an assistant professor at Bournemouth University (UK), a research fellow at Aberdeen University (UK), and a research engineer for Telefonica Research (Spain).
explanations, natural language generation, human-computer interaction, personalization (recommender systems), and intelligent user interfaces.
Dr Tintarev has published
over 40 peer reviewed papers. H-index: 15, most cited article >250 citations, > 1100 total (according to Google scholar on the 11/07/17), on the topic of personalized information presentation in different application areas.
In the last 2 years she has received grant awards as PI or Co-I totalling over €200K
. She has been a member of the ACM since January 2014.
Dr. Tintarev is a senior PC member for the following conferences:
She additionally reviews for many leading conferences and journals
in human-computer interaction (CHI), artificial intelligence (IJCAI-ECAI), and personalization (TiiS). She also serves in organizational roles
for international conferences and workshops. Dr. Tintarev examines PhD dissertations, and reviews grant proposals in the Netherlands (NWO), the UK (EPSRC), and Belgium (VLAIO).
19th of April.
Summary of The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction, with Ferro et al, accepted for publication in the SIGIR Forum (June)!
13th of April.
Effects of Individual Traits on Diversity-aware Music Recommender User Interfaces, full paper accepted to UMAP. With Yucheng Jin and Katrien Verbert
16th of February.
New paper: Adrian Holzer, Nava Tintarev, Samuel Bendahan, Shane Greenup, and Denis Gillet. Digitally Scaffolding Debate in the Classroom. Proceedings of the 2018 CHI Conference Extended Abstracts on Human Factors in Computing Systems.
12th of December.
New book chapter with Alexander Felfernig, Thi Ngoc Trang Tran, and Martin Stettinger: Explanations for Groups
, in handbook on Group Recommender Systems, Springer.
20th of November.
Paper accepted to Symposium on Applied Computing (SAC), Recommender Systems Track, Knowing the Unknown: Visualising Consumption Blind-Spots in Recommender Systems
, with Shahin Rostami and Barry Smyth.
10th of August.
Poster accepted to SEMANTiCS 2017: Mengmeng Ye, Christoph Lofi, and Nava Tintarev. Memorability of semantically grouped online reviews
18th of July.
Paper accepted to the FATREC Workshop on Responsible Recommendation, in association with Recsys'17: Presenting Challenging Recommendations: Making Diverse News Acceptable.
17th of July.
Case study with Blendle accepted to NWO ICT with Industry
on Personalized (and diverse) news selection. Seeking PhD students and Postdocs. November 27 - December 1, 2017.
10th of July.
Paper accepted with Christoph Lofi at the Complex-Rec workshop: Towards Analogy-based Recommendation: Benchmarking of Perceived Analogy Semantics.
5th of July.
Invited to attend the Royal Society's workshop on filter bubbles on the 19 July!
5th of July.
I am excited to be giving a keynote at the workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (SOAP)
in conjunction with UMAP 2017.
5th of July.
Approved grant proposal: Designing Artificial Advice Givers that Consider Perspective and Affect in Reasoning. Funded by the Delft Design for Values Open Subsidy.
5th of July.
Our position paper (with Pavel Kucherbaev, and Carlos Rodriguez) ``Ephemeral Context to Support Robust and Diverse Recommendations'' has been accepted to appear at the Machine Learning for Music Discovery workshop at ICML 2017.
14th of June.
Delighted to be joining the Dagstuhl Perspectives Workshop, ``Towards Cross-Domain Performance Modeling and Prediction: IR/RecSys/NLP''
later this year.