Tim Donkers
- Information Systems top 2%
- Recommender Systems and Techniques 13
- Artificial Intelligence top 5%
- Explainable Artificial Intelligence (XAI) 4
- Topic Modeling 4
- Advanced Text Analysis Techniques 4
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- Opinion Dynamics and Social Influence 3
- Complex Network Analysis Techniques 3
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- Social Media and Politics 2
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- Technology Adoption and User Behaviour 2
- Co-authors
- Jürgen ZieglerBenedikt LoeppJohannes Kunkel
- Journals
- International Journal of Human-Computer Studies (1 paper)i-com (1 paper)Datenbank-Spektrum (1 paper)
- Partner nations
- Germany
In The Last Decade
Tim Donkers
17 papers receiving 446 citations
Peers
Comparison fields: 5 of 66
- Information Systems 301
- Artificial Intelligence 292
- Health Informatics 9
- Management Science and Operations Research 70
- General Decision Sciences 10
Countries citing papers authored by Tim Donkers
This map shows the geographic impact of Tim Donkers'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 Tim Donkers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Donkers more than expected).
Fields of papers citing papers by Tim Donkers
This network shows the impact of papers produced by Tim Donkers. 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 Tim Donkers. The network helps show where Tim Donkers may publish in the future.
Co-authorship network
The 3 scholars most cited alongside Tim Donkers, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 4 | |
| 3 | 2023 | 0 | |
| 4 | 2023 | 4 | |
| 5 | 2021 | 29 | |
| 6 | 2020 | 2 | |
| 7 | 2020 | 11 | |
| 8 | 2020 | 17 | |
| 9 | 2019 | 89 | |
| 10 | 2019 | 2 | |
| 11 | Explaining Recommendations by Means of User Reviews. | 2018 | 7 |
| 12 | Trust-related Effects of Expertise and Similarity Cues in Human-Generated Recommendations. | 2018 | 5 |
| 13 | 2018 | 16 | |
| 14 | 2018 | 18 | |
| 15 | 2018 | 28 | |
| 16 | 2017 | 223 | |
| 17 | Towards Understanding Latent Factors and User Profiles by Enhancing Matrix Factorization with Tags. | 2016 | 3 |
| 18 | 2016 | 7 | |
| 19 | Merging Latent Factors and Tags to Increase Interactive Control of Recommendations | 2015 | 4 |
About Tim Donkers
Tim Donkers is a scholar working on General Decision Sciences, Health Informatics, Information Systems, Artificial Intelligence and Information Systems and Management, having authored 19 papers that have together received 469 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (13 papers), Explainable Artificial Intelligence (XAI) (4 papers), Topic Modeling (4 papers), Advanced Text Analysis Techniques (4 papers), Opinion Dynamics and Social Influence (3 papers), Complex Network Analysis Techniques (3 papers), Social Media and Politics (2 papers) and Technology Adoption and User Behaviour (2 papers). The work is most often cited by research in Information Systems (301 citations), Artificial Intelligence (292 citations), Health Informatics (9 citations), Management Science and Operations Research (70 citations) and General Decision Sciences (10 citations). Tim Donkers has collaborated with scholars based in Germany. Frequent co-authors include Jürgen Ziegler, Benedikt Loepp and Johannes Kunkel. Their work appears in journals such as International Journal of Human-Computer Studies, i-com, Datenbank-Spektrum, Proceedings of the International AAAI Conference on Web and Social Media and Conference on Recommender Systems.
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.