Chris Volinsky

33 papers receiving 13.4k citations

Hit Papers

Matrix Factorization Techniques for Recommender Systems 2009 · 7.0k citations
7.0k19992026200820172.0k4.0k6.0k

Peers

Chris Volinsky
Comparison fields: 5 of 208
  • Computational Mathematics 213
  • Information Systems 7.5k
  • Transportation 1.2k
  • Artificial Intelligence 5.6k
  • Management Science and Operations Research 2.0k
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Countries citing papers authored by Chris Volinsky

Since Specialization
Citations

This map shows the geographic impact of Chris Volinsky'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 Chris Volinsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris Volinsky more than expected).

Fields of papers citing papers by Chris Volinsky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chris Volinsky. 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 Chris Volinsky. The network helps show where Chris Volinsky may publish in the future.

Co-authors

The 25 scholars most cited alongside Chris Volinsky, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Chris Volinsky Line = papers co-authored together Chris Volinsky links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Matrix Factorization Techniques for Recommender Systems
Hit paper breakdown →
20097025
2
Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors
Hit paper breakdown →
19992965
3
Collaborative Filtering for Implicit Feedback Datasets
Hit paper breakdown →
20081945
4 2007227
5 2000221
6 2011205
7 2013204
8 2003201
9 1997163
10 2000121
11
The BellKor 2008 Solution to the Netflix Prize
200788
12 200683
13 199678
14 200176
15 199971
16 200369
17 201155
18 201036
19 201034
20 200934

About Chris Volinsky

Chris Volinsky is a scholar working on Statistics and Probability, Transportation, Statistical and Nonlinear Physics, Marketing and Information Systems, having authored 34 papers that have together received 14.1k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (7 papers), Recommender Systems and Techniques (6 papers), Statistical Methods and Bayesian Inference (4 papers), Consumer Market Behavior and Pricing (4 papers), Statistical Methods and Inference (4 papers), Data Mining Algorithms and Applications (4 papers), Human Mobility and Location-Based Analysis (4 papers) and Image Retrieval and Classification Techniques (3 papers). The work is most often cited by research in Computational Mathematics (213 citations), Information Systems (7.5k citations), Transportation (1.2k citations), Artificial Intelligence (5.6k citations) and Management Science and Operations Research (2.0k citations). Chris Volinsky has collaborated with scholars based in United States, Netherlands and Australia. Frequent co-authors include Yehuda Koren, Robert Bell, Adrian E. Raftery, David Madigan, Jennifer A. Hoeting, Yifan Hu, Richard A. Becker, Simon Urbanek, Ji Meng Loh and Ramón Cáceres. Their work appears in journals such as Statistical Science, Conservation Biology, Journal of Clinical and Experimental Neuropsychology, Communications of the ACM and Computational Statistics & 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.

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