Deep content-based music recommendation
- Journal
- Ghent University Academic Bibliography (Ghent University)
In The Last Decade
doi.org/w8703902 →Countries where authors are citing Deep content-based music recommendation
This map shows the geographic impact of Deep content-based music recommendation. 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 Deep content-based music recommendation with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep content-based music recommendation more than expected).
Fields of papers citing Deep content-based music recommendation
This network shows the impact of Deep content-based music recommendation. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep content-based music recommendation.
About Deep content-based music recommendation
This paper, published in 2013, received 629 indexed citations . Written by Aäron van den Oord, Sander Dieleman and Benjamin Schrauwen covering the research area of Computer Vision and Pattern Recognition and Signal Processing. It is primarily cited by scholars working on Information Systems (374 citations), Computer Vision and Pattern Recognition (300 citations) and Artificial Intelligence (272 citations). Published in Ghent University Academic Bibliography (Ghent University).
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.
This paper is also available at doi.org/w8703902.