Probabilistic Matrix Factorization
- Authors
- Andriy MnihRuslan Salakhutdinov
- Journal
- Neural Information Processing Systems
In The Last Decade
doi.org/w8245050 →Countries where authors are citing Probabilistic Matrix Factorization
This map shows the geographic impact of Probabilistic Matrix Factorization. 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 Probabilistic Matrix Factorization with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Probabilistic Matrix Factorization more than expected).
Fields of papers citing Probabilistic Matrix Factorization
This network shows the impact of Probabilistic Matrix Factorization. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Probabilistic Matrix Factorization.
About Probabilistic Matrix Factorization
This paper, published in 2007, received 2.4k indexed citations . Written by Andriy Mnih and Ruslan Salakhutdinov covering the research area of Computer Science Applications, Artificial Intelligence and Information Systems. It is primarily cited by scholars working on Information Systems (1.9k citations), Artificial Intelligence (1.2k citations), Computer Vision and Pattern Recognition (654 citations), Computer Networks and Communications (375 citations) and Management Science and Operations Research (336 citations). Published in Neural Information Processing 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.
This paper is also available at doi.org/w8245050.