Lukas Lerche
Impact in
- Information Systems top 2%
- Recommender Systems and Techniques
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- Advanced Bandit Algorithms Research
Papers in
-
- Recommender Systems and Techniques 15
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- Advanced Bandit Algorithms Research 9
- Co-authors
- Dietmar Jannach (16 shared papers)Michael Jugovac (7 shared papers)Iman Kamehkhosh (4 shared papers)Malte Ludewig (2 shared papers)
- Journals
- User Modeling and User-Adapted Interaction (2 papers)Expert Systems with Applications (1 paper)ACM Transactions on Interactive Intelligent Systems (1 paper)i-com (1 paper)Technische Universität Dortmund Eldorado (Technische Universität Dortmund) (1 paper)
- Partner nations
- Germany
In The Last Decade
Lukas Lerche
15 papers receiving 520 citations
Peers
Comparison fields: 5 of 49
- Information Systems 452
- Management Science and Operations Research 189
- Marketing 90
- Signal Processing 78
- Artificial Intelligence 219
Countries citing papers authored by Lukas Lerche
This map shows the geographic impact of Lukas Lerche'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 Lukas Lerche with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lukas Lerche more than expected).
Fields of papers citing papers by Lukas Lerche
This network shows the impact of papers produced by Lukas Lerche. 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 Lukas Lerche. The network helps show where Lukas Lerche may publish in the future.
Co-authors
The 4 scholars most cited alongside Lukas Lerche, 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 | 2015 | 139 | |
| 2 | 2017 | 88 | |
| 3 | 2015 | 76 | |
| 4 | 2014 | 52 | |
| 5 | 2017 | 47 | |
| 6 | 2016 | 38 | |
| 7 | 2015 | 34 | |
| 8 | 2017 | 20 | |
| 9 | 2015 | 11 | |
| 10 | 2016 | 10 | |
| 11 | 2016 | 6 | |
| 12 | Personalized Next-Track Music Recommendation with Multi-dimensional Long-Term Preference Signals. | 2016 | 5 |
| 13 | Item Familiarity Effects in User-Centric Evaluations of Recommender Systems | 2015 | 4 |
| 14 | Re-Ranking Recommendations Based on Predicted Short-Term Interests - A Protocol and First Experiment | 2013 | 4 |
| 15 | 2015 | 4 | |
| 16 | 2013 | 0 | |
| 17 | 2017 | 0 |
About Lukas Lerche
Lukas Lerche is a scholar working on Information Systems, Management Science and Operations Research, Computer Vision and Pattern Recognition, Marketing and Signal Processing, having authored 17 papers that have together received 538 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (15 papers), Advanced Bandit Algorithms Research (9 papers), Consumer Market Behavior and Pricing (4 papers), Music Technology and Sound Studies (3 papers), Music and Audio Processing (3 papers), Data Stream Mining Techniques (1 paper), Decision-Making and Behavioral Economics (1 paper) and Advanced Wireless Network Optimization (1 paper). The work is most often cited by research in Information Systems (452 citations), Management Science and Operations Research (189 citations), Marketing (90 citations), Signal Processing (78 citations) and Artificial Intelligence (219 citations). Lukas Lerche has collaborated with scholars based in Germany. Frequent co-authors include Dietmar Jannach, Michael Jugovac, Iman Kamehkhosh and Malte Ludewig. Their work appears in journals such as User Modeling and User-Adapted Interaction, Expert Systems with Applications, ACM Transactions on Interactive Intelligent Systems, i-com and Technische Universität Dortmund Eldorado (Technische Universität Dortmund).
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.