Kelong Mao

965 total citations · 1 hit paper
29 papers, 473 citations indexed

About

Kelong Mao is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Kelong Mao has authored 29 papers receiving a total of 473 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 5 papers in Computer Networks and Communications. Recurrent topics in Kelong Mao's work include Topic Modeling (18 papers), Natural Language Processing Techniques (10 papers) and Speech and dialogue systems (7 papers). Kelong Mao is often cited by papers focused on Topic Modeling (18 papers), Natural Language Processing Techniques (10 papers) and Speech and dialogue systems (7 papers). Kelong Mao collaborates with scholars based in China, Canada and Hong Kong. Kelong Mao's co-authors include Xi Xiao, Jieming Zhu, Xiuqiang He, Biao Lu, Zhaowei Wang, Zhicheng Dou, Zhenhua Dong, Quanyu Dai, Jinpeng Wang and Yu Rong and has published in prestigious journals such as Neurocomputing, ACM Transactions on Information Systems and IEEE Transactions on Dependable and Secure Computing.

In The Last Decade

Kelong Mao

26 papers receiving 468 citations

Hit Papers

UltraGCN 2021 2026 2022 2024 2021 50 100 150
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.

  1. UltraGCN
    2021 182 citations Kelong Mao, Xi Xiao et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kelong Mao China 10 337 292 102 51 29 29 473
Xiaotian Han China 7 306 0.9× 237 0.8× 66 0.6× 56 1.1× 10 0.3× 10 414
Long-Kai Huang China 9 278 0.8× 195 0.7× 159 1.6× 51 1.0× 13 0.4× 18 447
Fangli Xu China 10 311 0.9× 176 0.6× 87 0.9× 30 0.6× 7 0.2× 17 401
Yuhan Quan China 5 344 1.0× 342 1.2× 109 1.1× 53 1.0× 6 0.2× 7 498
Zuohui Fu United States 16 570 1.7× 414 1.4× 124 1.2× 45 0.9× 7 0.2× 31 731
Jiancan Wu China 11 262 0.8× 196 0.7× 59 0.6× 22 0.4× 8 0.3× 26 374
Jianxin Ma China 10 448 1.3× 331 1.1× 127 1.2× 86 1.7× 7 0.2× 15 599
Houye Ji China 8 510 1.5× 159 0.5× 112 1.1× 38 0.7× 9 0.3× 10 593
Linmei Hu China 6 366 1.1× 195 0.7× 53 0.5× 52 1.0× 8 0.3× 8 424
Jianhui Ma China 11 251 0.7× 104 0.4× 63 0.6× 18 0.4× 10 0.3× 29 381

Countries citing papers authored by Kelong Mao

Since Specialization
Citations

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

Fields of papers citing papers by Kelong Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kelong Mao

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

All Works

20 of 20 papers shown
1.
2.
Mao, Kelong, et al.. (2025). A Survey of Conversational Search. ACM Transactions on Information Systems. 43(6). 1–50.
3.
Liu, Zheng, et al.. (2025). MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation. 2366–2377. 1 indexed citations
4.
Mao, Kelong, Chenlong Deng, Haonan Chen, et al.. (2024). ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval. 1227–1240. 3 indexed citations
5.
Mao, Kelong, et al.. (2024). Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session Embedding. 2879–2893. 2 indexed citations
6.
Mao, Kelong, et al.. (2024). History-Aware Conversational Dense Retrieval. Research at the University of Copenhagen (University of Copenhagen). 13366–13378. 1 indexed citations
7.
Mao, Kelong, et al.. (2024). CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search. 2253–2268. 3 indexed citations
8.
9.
Mao, Kelong, et al.. (2024). ConvSDG: Session Data Generation for Conversational Search. 1634–1642. 1 indexed citations
10.
Xiao, Xi, Shuo Wang, Guangwu Hu, et al.. (2024). RBLJAN: Robust Byte-Label Joint Attention Network for Network Traffic Classification. IEEE Transactions on Dependable and Secure Computing. 22(3). 2161–2178. 1 indexed citations
11.
Mao, Kelong, et al.. (2024). Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search. Research at the University of Copenhagen (University of Copenhagen). 1700–1710. 2 indexed citations
12.
Chen, Haonan, et al.. (2024). Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation. 2700–2718. 2 indexed citations
13.
Deng, Chenlong, Kelong Mao, & Zhicheng Dou. (2024). Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation. 1253–1265.
14.
Liu, Zheng, et al.. (2024). Grounding Language Model with Chunking-Free In-Context Retrieval. 1298–1311. 4 indexed citations
15.
Mao, Kelong, et al.. (2023). Learning Denoised and Interpretable Session Representation for Conversational Search. 3193–3202. 10 indexed citations
16.
Mao, Kelong, et al.. (2023). FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction. Proceedings of the AAAI Conference on Artificial Intelligence. 37(4). 4552–4560. 38 indexed citations
17.
Nie, Jian‐Yun, Kaiyu Huang, Kelong Mao, et al.. (2023). Learning to Relate to Previous Turns in Conversational Search. 1722–1732. 10 indexed citations
18.
Mao, Kelong, et al.. (2023). Search-Oriented Conversational Query Editing. 4160–4172. 7 indexed citations
19.
Mao, Kelong, et al.. (2023). ConvGQR: Generative Query Reformulation for Conversational Search. 4998–5012. 16 indexed citations
20.
Mao, Kelong, et al.. (2022). ConvTrans: Transforming Web Search Sessions for Conversational Dense Retrieval. 2935–2946. 9 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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