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.
A literature review and classification of recommender systems research
2012430 citationsHyea Kyeong Kim, Il Young Choi et al.Expert Systems with Applicationsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Hyea Kyeong Kim
Since
Specialization
Citations
This map shows the geographic impact of Hyea Kyeong Kim'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 Hyea Kyeong Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hyea Kyeong Kim more than expected).
This network shows the impact of papers produced by Hyea Kyeong Kim. 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 Hyea Kyeong Kim. The network helps show where Hyea Kyeong Kim may publish in the future.
Co-authorship network of co-authors of Hyea Kyeong Kim
This figure shows the co-authorship network connecting the top 25 collaborators of Hyea Kyeong Kim.
A scholar is included among the top collaborators of Hyea Kyeong Kim 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 Hyea Kyeong Kim. Hyea Kyeong Kim is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kim, Hyea Kyeong, et al.. (2012). A literature review and classification of recommender systems research. Expert Systems with Applications. 39(11). 10059–10072.430 indexed citations breakdown →
Choi, Il Young, Hyea Kyeong Kim, & Jae Kyeong Kim. (2011). A personalized booth recommendation using Bayesian networks in ubiquitous exhibition. 14(9). 2973–2989.
7.
Kim, Jae Kyeong, et al.. (2009). A New Item Recommendation Procedure Using Preference Boundary. 273–280.1 indexed citations
8.
Kim, Hyea Kyeong, et al.. (2009). A Network Approach to Derive Product Relations and Analyze Topological Characteristics. Journal of Intelligence and Information Systems. 15(4). 159–182.2 indexed citations
9.
Kim, Jaekyeong, et al.. (2009). A Study on Blog users’ Response to Blog Marketing. 11(3). 1–17.
Kim, Jaekyeong & Hyea Kyeong Kim. (2008). Agricultural and stockbreeding products recommender system using RFID based traceability system. Journal of Intelligence and Information Systems. 14(2). 207–222.2 indexed citations
14.
Kim, Jae Kyeong, et al.. (2006). A Hybrid Multimedia Contents Recommendation Procedure for a New Item Problem in M-commerce. Journal of Intelligence and Information Systems. 12(2). 1–15.2 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.