How Jing

1.0k total citations · 1 hit paper
11 papers, 606 citations indexed

About

How Jing is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, How Jing has authored 11 papers receiving a total of 606 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 5 papers in Information Systems and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in How Jing's work include Topic Modeling (4 papers), Recommender Systems and Techniques (4 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). How Jing is often cited by papers focused on Topic Modeling (4 papers), Recommender Systems and Techniques (4 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). How Jing collaborates with scholars based in United States and Taiwan. How Jing's co-authors include Alexander J. Smola, Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Yu Tsao, Jaewon Yang, Qi He, Liangyue Li, Bee-Chung Chen and Hanghang Tong and has published in prestigious journals such as International Joint Conference on Natural Language Processing.

In The Last Decade

How Jing

11 papers receiving 584 citations

Hit Papers

align trajectories

What are hit papers?

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.

Recurrent Recommender Networks

2017 416 citations Chao-Yuan Wu, Amr Ahmed et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
How Jing United States	6	470	378	140	124	63	11	606
Shengxian Wan China	6	378 0.8×	516 1.4×	170 1.2×	87 0.7×	59 0.9×	6	704
Ali Elkahky United States	6	471 1.0×	431 1.1×	210 1.5×	78 0.6×	33 0.5×	10	661
Christopher DuBois United States	6	611 1.3×	491 1.3×	249 1.8×	147 1.2×	57 0.9×	10	812
Zihan Lin China	9	533 1.1×	459 1.2×	129 0.9×	102 0.8×	49 0.8×	32	655
Fei Cai China	16	463 1.0×	543 1.4×	116 0.8×	106 0.9×	28 0.4×	55	738
Zhankui He United States	9	549 1.2×	515 1.4×	214 1.5×	92 0.7×	33 0.5×	23	743
Dong‐Kyu Chae South Korea	13	383 0.8×	343 0.9×	162 1.2×	71 0.6×	27 0.4×	56	624
Na Mou China	3	528 1.1×	325 0.9×	229 1.6×	106 0.9×	39 0.6×	7	628
Zhuoye Ding China	13	429 0.9×	434 1.1×	132 0.9×	136 1.1×	35 0.6×	35	670

Countries citing papers authored by How Jing

Since Specialization

Citations

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

Fields of papers citing papers by How Jing

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of How Jing

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

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

1.

Xiao, Yan, Ada Ma, Jaewon Yang, et al.. (2021). Contextual Skill Proficiency via Multi-task Learning at LinkedIn. 4273–4282. 3 indexed citations

2.

Shi, Baoxu, Jaewon Yang, Tim Weninger, How Jing, & Qi He. (2019). Representation Learning in Heterogeneous Professional Social Networks with Ambiguous Social Connections. 11. 1928–1937. 5 indexed citations

3.

Jing, How & Alexander J. Smola. (2017). Neural Survival Recommender. 515–524. 90 indexed citations

4.

Wu, Chao-Yuan, Amr Ahmed, Alex Beutel, Alexander J. Smola, & How Jing. (2017). Recurrent Recommender Networks. 495–503. 416 indexed citations breakdown →

5.

Li, Liangyue, How Jing, Hanghang Tong, et al.. (2017). NEMO. 505–513. 41 indexed citations

6.

Jing, How, et al.. (2014). A Transfer Probabilistic Collective Factorization Model to Handle Sparse Data in Collaborative Filtering. 20. 250–259. 15 indexed citations

7.

Jing, How & Shou-De Lin. (2014). Neural Conditional Energy Models for Multi-label Classification. 12. 240–249. 2 indexed citations

8.

Jing, How, et al.. (2014). Ensemble of machine learning algorithms for cognitive and physical speaker load detection. 447–451. 4 indexed citations

9.

Jing, How, Yu Tsao, Kuan‐Yu Chen, & Hsin‐Min Wang. (2013). Semantic Na"ive Bayes Classifier for Document Classification. International Joint Conference on Natural Language Processing. 1117–1123. 8 indexed citations

10.

Lee, Hung-yi, et al.. (2013). Ensemble of machine learning and acoustic segment model techniques for speech emotion and autism spectrum disorders recognition. 215–219. 20 indexed citations

11.

Jing, How & Yu Tsao. (2013). Sparse maximum entropy deep belief nets. 22. 1–6. 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.

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