Shaoyun Shi

525 total citations
15 papers, 273 citations indexed

About

Shaoyun Shi is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research. According to data from OpenAlex, Shaoyun Shi has authored 15 papers receiving a total of 273 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 8 papers in Information Systems and 4 papers in Management Science and Operations Research. Recurrent topics in Shaoyun Shi's work include Topic Modeling (8 papers), Recommender Systems and Techniques (7 papers) and Advanced Graph Neural Networks (6 papers). Shaoyun Shi is often cited by papers focused on Topic Modeling (8 papers), Recommender Systems and Techniques (7 papers) and Advanced Graph Neural Networks (6 papers). Shaoyun Shi collaborates with scholars based in China, United States and Hong Kong. Shaoyun Shi's co-authors include Haiyan Wu, Haiyu Song, Yiqun Liu, Shaoping Ma, Zhiqiang Zhang, Weizhi Ma, Hanxiong Chen, Min Zhang, Jiaxin Mao and Yongfeng Zhang and has published in prestigious journals such as Knowledge-Based Systems, ACM Transactions on Information Systems and Journal of Applied Mathematics.

In The Last Decade

Shaoyun Shi

13 papers receiving 269 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shaoyun Shi China	9	220	134	37	31	16	15	273
Marcel Genzmehr Germany	6	136 0.6×	199 1.5×	48 1.3×	39 1.3×	15 0.9×	7	252
Maurizio Ferrari Dacrema Italy	8	103 0.5×	93 0.7×	42 1.1×	32 1.0×	16 1.0×	26	187
Danyang Liu China	5	202 0.9×	195 1.5×	44 1.2×	22 0.7×	25 1.6×	7	276
Deqing Yang China	9	177 0.8×	120 0.9×	35 0.9×	28 0.9×	17 1.1×	34	232
Gabriel de Souza Pereira Moreira United States	7	144 0.7×	185 1.4×	47 1.3×	38 1.2×	14 0.9×	18	220
Maryam Karimzadehgan United States	8	144 0.7×	206 1.5×	29 0.8×	30 1.0×	15 0.9×	11	240
Lemei Zhang Norway	6	201 0.9×	184 1.4×	47 1.3×	24 0.8×	13 0.8×	9	260
Xiaokun Zhang China	9	145 0.7×	155 1.2×	41 1.1×	38 1.2×	6 0.4×	22	215
Keqin Bao China	7	242 1.1×	224 1.7×	48 1.3×	22 0.7×	10 0.6×	17	304

Countries citing papers authored by Shaoyun Shi

Since Specialization

Citations

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

Fields of papers citing papers by Shaoyun Shi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shaoyun Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Shaoyun Shi. A scholar is included among the top collaborators of Shaoyun Shi 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 Shaoyun Shi. Shaoyun Shi 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:

15 of 15 papers shown

1.

Shi, Shaoyun, et al.. (2025). DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System. 6217–6224.

2.

Shi, Shaoyun, et al.. (2024). Enhancing Recommendation Accuracy and Diversity with Box Embedding: A Universal Framework. 3756–3766. 1 indexed citations

3.

Shi, Shaoyun, et al.. (2024). Feature-Enhanced Neural Collaborative Reasoning for Explainable Recommendation. ACM Transactions on Information Systems. 43(1). 1–33.

4.

Shi, Shaoyun, Yuexiang Xie, Zhen Wang, et al.. (2022). Explainable Neural Rule Learning. Proceedings of the ACM Web Conference 2022. 3031–3041. 8 indexed citations

5.

Chen, Hanxiong, Yunqi Li, Shaoyun Shi, et al.. (2022). Graph Collaborative Reasoning. arXiv (Cornell University). 75–84. 19 indexed citations

6.

Wu, Haiyan, et al.. (2021). Phrase dependency relational graph attention network for Aspect-based Sentiment Analysis. Knowledge-Based Systems. 236. 107736–107736. 80 indexed citations

7.

Wu, Haiyan, et al.. (2021). Key n -Gram Extractions and Analyses of Different Registers Based on Attention Network. Journal of Applied Mathematics. 2021. 1–16. 1 indexed citations

8.

Shi, Shaoyun, Weizhi Ma, Zhen Wang, et al.. (2021). WG4Rec. 1651–1660. 12 indexed citations

9.

Wu, Haiyan, Ying Liu, & Shaoyun Shi. (2020). Modularized Syntactic Neural Networks for Sentence Classification. 2786–2792. 5 indexed citations

10.

Shi, Shaoyun, Weizhi Ma, Min Zhang, et al.. (2020). Beyond User Embedding Matrix. 319–328. 17 indexed citations

11.

Shi, Shaoyun, Hanxiong Chen, Weizhi Ma, et al.. (2020). Neural Logic Reasoning. 1365–1374. 54 indexed citations

12.

Shi, Shaoyun, et al.. (2019). Adaptive Feature Sampling for Recommendation with Missing Content Feature Values. 1451–1460. 17 indexed citations

13.

Shi, Shaoyun, Min Zhang, Yiqun Liu, & Shaoping Ma. (2018). Attention-based Adaptive Model to Unify Warm and Cold Starts Recommendation. 127–136. 37 indexed citations

14.

Wang, Yuntao, Chun Yu, Shaoyun Shi, et al.. (2017). ViVo. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 5568–5579. 13 indexed citations

15.

Shi, Shaoyun, Bin Hao, Bin Liu, et al.. (2017). How Integration helps on Cold-Start Recommendations. 1–6. 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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