Erion Çano
Impact in
- Information Systems top 5%
- Recommender Systems and Techniques
- Artificial Intelligence top 10%
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Topic Modeling
Papers in
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- Advanced Text Analysis Techniques 6
- Topic Modeling 6
- Sentiment Analysis and Opinion Mining 4
- Neural Networks and Applications 2
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- Music and Audio Processing 5
- Co-authors
- Maurizio Morisio (10 shared papers)Ondřej Bojar (4 shared papers)Marco Marengo (1 shared paper)Riccardo Coppola (1 shared paper)Benjamin Roth (2 shared papers)Sahand Sharifzadeh (1 shared paper)Jutta L. Mueller (1 shared paper)Stefan Schweter (1 shared paper)
In The Last Decade
Erion Çano
17 papers receiving 378 citations
Erion Çano's Hit Papers
Peers
Comparison fields: 5 of 53
- Information Systems 219
- Artificial Intelligence 203
- Signal Processing 64
- Computer Vision and Pattern Recognition 86
- Music 10
Countries citing papers authored by Erion Çano
This map shows the geographic impact of Erion Çano'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 Erion Çano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erion Çano more than expected).
Fields of papers citing papers by Erion Çano
This network shows the impact of papers produced by Erion Çano. 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 Erion Çano. The network helps show where Erion Çano may publish in the future.
Co-authors
The 9 scholars most cited alongside Erion Çano, 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 | Hybrid recommender systems: A systematic literature review Hit paper breakdown → | 2017 | 245 |
| 2 | 2017 | 27 | |
| 3 | 2017 | 17 | |
| 4 | 2019 | 16 | |
| 5 | 2015 | 11 | |
| 6 | 2018 | 11 | |
| 7 | 2017 | 11 | |
| 8 | 2017 | 10 | |
| 9 | 2022 | 9 | |
| 10 | Keyphrase Generation: A Multi-Aspect Survey | 2019 | 9 |
| 11 | 2023 | 8 | |
| 12 | 2018 | 6 | |
| 13 | 2018 | 5 | |
| 14 | 2017 | 5 | |
| 15 | 2019 | 3 | |
| 16 | 2014 | 1 | |
| 17 | 2023 | 1 | |
| 18 | Two Huge Title and Keyword Generation Corpora of Research Articles | 2020 | 0 |
| 19 | 2025 | 0 |
About Erion Çano
Erion Çano is a scholar working on Artificial Intelligence, Signal Processing, Information Systems, Computer Vision and Pattern Recognition and Cognitive Neuroscience, having authored 19 papers that have together received 395 indexed citations. Recurring topics across this work include Advanced Text Analysis Techniques (6 papers), Topic Modeling (6 papers), Music and Audio Processing (5 papers), Sentiment Analysis and Opinion Mining (4 papers), Neural Networks and Applications (2 papers), Music Technology and Sound Studies (2 papers), Information Retrieval and Search Behavior (2 papers) and Recommender Systems and Techniques (2 papers). The work is most often cited by research in Information Systems (219 citations), Artificial Intelligence (203 citations), Signal Processing (64 citations), Computer Vision and Pattern Recognition (86 citations) and Music (10 citations). Erion Çano has collaborated with scholars based in Italy, Czechia and Austria. Frequent co-authors include Maurizio Morisio, Ondřej Bojar, Marco Marengo, Riccardo Coppola, Benjamin Roth, Sahand Sharifzadeh, Jutta L. Mueller, Stefan Schweter and Ivan Habernal. Their work appears in journals such as ACM Computing Surveys, Language Resources and Evaluation, Data Technologies and Applications, Lecture notes in computer science and Intelligent Data Analysis.
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.