Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Hybrid Recommender Systems: Survey and Experiments
This map shows the geographic impact of Robin Burke'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 Robin Burke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robin Burke more than expected).
This network shows the impact of papers produced by Robin Burke. 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 Robin Burke. The network helps show where Robin Burke may publish in the future.
Co-authorship network of co-authors of Robin Burke
This figure shows the co-authorship network connecting the top 25 collaborators of Robin Burke.
A scholar is included among the top collaborators of Robin Burke based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Robin Burke. Robin Burke is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Burke, Robin, et al.. (2016). Weighted Random Walks for Meta-Path Expansion in Heterogeneous Networks.. Conference on Recommender Systems.3 indexed citations
8.
Mobasher, Bamshad, et al.. (2016). User Segmentation for Controlling Recommendation Diversity.. Conference on Recommender Systems.3 indexed citations
9.
Zheng, Yong, Bamshad Mobasher, & Robin Burke. (2015). Incorporating context correlation into context-aware matrix factorization. International Joint Conference on Artificial Intelligence. 21–27.13 indexed citations
10.
Burke, Robin, et al.. (2013). Social Web Recommendation using Metapaths.. Conference on Recommender Systems.7 indexed citations
Mobasher, Bamshad, et al.. (2008). A Survey of Collaborative Recommendation and the Robustness of Model-Based Algorithms.. IEEE Data(base) Engineering Bulletin. 31. 3–13.43 indexed citations
13.
Bhaumik, Runa, Robin Burke, & Bamshad Mobasher. (2007). Crawling Attacks Against Web-based Recommender Systems.. 183–189.3 indexed citations
14.
Bhaumik, Runa, Chad Williams, Bamshad Mobasher, & Robin Burke. (2006). Securing collaborative filtering against malicious attacks through anomaly detection. National Conference on Artificial Intelligence. 50–59.56 indexed citations
15.
Burke, Robin. (1999). The Wasabi Personal Shopper: a case-based recommender system. National Conference on Artificial Intelligence. 844–849.61 indexed citations
Cohn, David, David M. G. Lewis, David W. Aha, et al.. (1996). The 1995 Fall Symposia Series. AI Magazine. 17(1). 83.
18.
Burke, Robin, et al.. (1996). Knowledge-based navigation of complex information spaces. National Conference on Artificial Intelligence. 462–468.109 indexed citations
19.
Hammond, Kristian J. & Robin Burke. (1995). Combining databases and knowledge bases for assisted browsing. National Conference on Artificial Intelligence.3 indexed citations
20.
Burke, Robin. (1993). Intelligent Retrieval of Video Stories in a Social Simulation.. Journal of educational multimedia and hypermedia. 2(4).1 indexed citations
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.