Keping Yang

1.3k total citations
16 papers, 420 citations indexed

About

Keping Yang is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Keping Yang has authored 16 papers receiving a total of 420 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Information Systems, 11 papers in Artificial Intelligence and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Keping Yang's work include Recommender Systems and Techniques (10 papers), Sentiment Analysis and Opinion Mining (4 papers) and Web Data Mining and Analysis (3 papers). Keping Yang is often cited by papers focused on Recommender Systems and Techniques (10 papers), Sentiment Analysis and Opinion Mining (4 papers) and Web Data Mining and Analysis (3 papers). Keping Yang collaborates with scholars based in China, Hong Kong and United States. Keping Yang's co-authors include Quan Lin, Hong Wen, Fuyu Lv, Quan Yuan, Ningxia Wang, Li Chen, Wilfred Ng, Fei Sun, Xusheng Luo and Xiao-Yang Liu and has published in prestigious journals such as Pattern Recognition, Rare & Special e-Zone (The Hong Kong University of Science and Technology) and arXiv (Cornell University).

In The Last Decade

Keping Yang

15 papers receiving 399 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Keping Yang China 10 286 267 90 89 39 16 420
Benedikt Loepp Germany 8 332 1.2× 230 0.9× 126 1.4× 83 0.9× 39 1.0× 22 432
Justin Basilico United States 9 279 1.0× 181 0.7× 111 1.2× 94 1.1× 41 1.1× 14 432
Hande Dong China 3 271 0.9× 251 0.9× 74 0.8× 102 1.1× 22 0.6× 4 404
Fatih Gedikli Germany 7 285 1.0× 214 0.8× 99 1.1× 56 0.6× 71 1.8× 13 405
Marcelo Garcia Manzato Brazil 13 297 1.0× 193 0.7× 136 1.5× 75 0.8× 46 1.2× 71 389
Manolis G. Vozalis Greece 6 281 1.0× 291 1.1× 94 1.0× 58 0.7× 92 2.4× 8 506
Sheetal Girase India 6 230 0.8× 119 0.4× 88 1.0× 36 0.4× 28 0.7× 14 295
Ilya Markov Netherlands 12 366 1.3× 256 1.0× 118 1.3× 113 1.3× 20 0.5× 31 533

Countries citing papers authored by Keping Yang

Since Specialization
Citations

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

Fields of papers citing papers by Keping Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Keping Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Keping Yang. A scholar is included among the top collaborators of Keping Yang 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 Keping Yang. Keping Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Dai, Quanyu, Xiao-Ming Wu, Lu Fan, et al.. (2022). Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks. Pattern Recognition. 128. 108628–108628. 31 indexed citations
2.
Tan, Jiwei, et al.. (2022). ReprBERT: Distilling BERT to an Efficient Representation-Based Relevance Model for E-Commerce. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4363–4371. 9 indexed citations
3.
Zhang, Junhao, Weidi Xu, Jianhui Ji, et al.. (2021). Modeling Across-Context Attention For Long-Tail Query Classification in E-commerce. 58–66. 7 indexed citations
4.
Luo, Xusheng, et al.. (2021). AliCoCo2: Commonsense Knowledge Extraction, Representation and Application in E-commerce. 3385–3393. 14 indexed citations
5.
Li, Sen, Fuyu Lv, Keping Yang, et al.. (2021). Embedding-based Product Retrieval in Taobao Search. 3181–3189. 33 indexed citations
6.
7.
Wen, Hong, et al.. (2020). GMCM: Graph-based Micro-behavior Conversion Model for Post-click Conversion Rate Estimation. 2201–2210. 16 indexed citations
8.
Luo, Xusheng, et al.. (2020). AliCoCo: Alibaba E-commerce Cognitive Concept Net. 313–327. 43 indexed citations
9.
Wen, Hong, et al.. (2019). Conversion Rate Prediction via Post-Click Behaviour Modeling. 2 indexed citations
10.
Wen, Hong, Jing Zhang, Quan Lin, Keping Yang, & Pipei Huang. (2019). Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 338–345. 26 indexed citations
11.
12.
Liu, Xiaoyang, et al.. (2019). A Causal Perspective to Unbiased Conversion Rate Estimation on Data Missing Not at Random. 1 indexed citations
13.
Lv, Fuyu, et al.. (2019). SDM. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 2635–2643. 88 indexed citations
14.
Luo, Xusheng, et al.. (2019). Conceptualize and Infer User Needs in E-commerce. 2517–2525. 9 indexed citations
15.
Wen, Hong, Jing Zhang, Quan Lin, et al.. (2018). Multi-Level Deep Cascade Trees for Conversion Rate Prediction.. arXiv (Cornell University). 1 indexed citations
16.
Yang, Keping, et al.. (2001). Technical Trend of Online Game Server. 16(4). 14–22.

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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