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.
Mining interesting locations and travel sequences from GPS trajectories
20091.3k citationsXing Xie, Wei‐Ying Ma et al.profile →
A survey of content-based image retrieval with high-level semantics
This map shows the geographic impact of Wei‐Ying Ma'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 Wei‐Ying Ma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wei‐Ying Ma more than expected).
This network shows the impact of papers produced by Wei‐Ying Ma. 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 Wei‐Ying Ma. The network helps show where Wei‐Ying Ma may publish in the future.
Co-authorship network of co-authors of Wei‐Ying Ma
This figure shows the co-authorship network connecting the top 25 collaborators of Wei‐Ying Ma.
A scholar is included among the top collaborators of Wei‐Ying Ma 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 Wei‐Ying Ma. Wei‐Ying Ma is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ma, Wei‐Ying, Tiejun Zhao, Kuiyuan Yang, Wei Yu, & Yalong Bai. (2013). Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data. arXiv (Cornell University).1 indexed citations
5.
Huai, Jinpeng, Hsiao-Wuen Hon, Yunhao Liu, et al.. (2008). Proceedings of the 17th international conference on World Wide Web.14 indexed citations
6.
Nie, Zaiqing, Ji-Rong Wen, & Wei‐Ying Ma. (2007). Object-level vertical search. Conference on Innovative Data Systems Research. 235–246.70 indexed citations
Huang, Shen, Yong Yu, Gui-Rong Xue, et al.. (2006). TSSP: Multi-features based reinforcement algorithm to find related papers. Web Intelligence and Agent Systems An International Journal. 4(3). 271–287.5 indexed citations
9.
Zhang, Qi, et al.. (2006). Detecting Geographical Serving Area of Web Resources.5 indexed citations
Lao, Ni, Ji-Rong Wen, Wei‐Ying Ma, & Yi‐Min Wang. (2004). Combining High Level Symptom Descriptions and Low Level State Information for Configuration Fault Diagnosis. USENIX Large Installation Systems Administration Conference. 151–158.14 indexed citations
Song, Ruihua, Ji-Rong Wen, Shuming Shi, et al.. (2004). Microsoft Research Asia at Web Track and Terabyte Track of TREC 2004.. Text REtrieval Conference.40 indexed citations
16.
Ma, Wei‐Ying, et al.. (2003). Media Companion: Delivering Content-oriented Web Services to Internet Media. 11.
17.
Xue, Gui-Rong, et al.. (2003). User Access Pattern Enhanced Small Web Search. 10.5 indexed citations
18.
Chen, Liqun, et al.. (2003). DRESS: A Slicing Tree Based Web Page Representation for Various Display Sizes.. 9.17 indexed citations
19.
Chen, Li‐Qun, Xing Xie, Wei‐Ying Ma, Hao Zhang, & He-Qin Zhou. (2003). Image Adaptation Based on Attention Model for Small-Form-Factor Device.. 421.12 indexed citations
20.
Liu, Tao, Shengping Liu, Zheng Chen, & Wei‐Ying Ma. (2003). An evaluation on feature selection for text clustering. International Conference on Machine Learning. 488–495.161 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.