Leyu Lin

2.5k total citations · 1 hit paper
60 papers, 1.4k citations indexed

About

Leyu Lin is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Leyu Lin has authored 60 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Artificial Intelligence, 43 papers in Information Systems and 21 papers in Computer Vision and Pattern Recognition. Recurrent topics in Leyu Lin's work include Recommender Systems and Techniques (41 papers), Advanced Graph Neural Networks (26 papers) and Topic Modeling (22 papers). Leyu Lin is often cited by papers focused on Recommender Systems and Techniques (41 papers), Advanced Graph Neural Networks (26 papers) and Topic Modeling (22 papers). Leyu Lin collaborates with scholars based in China, United States and Hong Kong. Leyu Lin's co-authors include Ruobing Xie, Zhiyuan Liu, Bo Zhang, Xu Zhang, Feng Xia, Fen Lin, Maosong Sun, Fuzhen Zhuang, Yongchun Zhu and Shukai Liu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Information Sciences and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

Leyu Lin

59 papers receiving 1.3k 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.

Personalized Transfer of User Preferences for Cross-domain Recommendation

2022 132 citations Yongchun Zhu, Fuzhen Zhuang et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Leyu Lin China	23	1.0k	877	269	268	107	60	1.4k
Jianxun Lian China	16	814 0.8×	897 1.0×	161 0.6×	247 0.9×	110 1.0×	42	1.2k
Ruobing Xie China	26	2.1k 2.0×	934 1.1×	577 2.1×	400 1.5×	102 1.0×	99	2.4k
Fajie Yuan China	16	815 0.8×	925 1.1×	224 0.8×	318 1.2×	62 0.6×	45	1.2k
Pipei Huang China	7	549 0.5×	654 0.7×	120 0.4×	299 1.1×	125 1.2×	10	952
Qinyong Wang China	15	1.1k 1.1×	1.2k 1.4×	215 0.8×	330 1.2×	184 1.7×	26	1.6k
Lucas Drumond Germany	13	534 0.5×	593 0.7×	149 0.6×	175 0.7×	94 0.9×	32	914
Laurent Charlin Canada	13	695 0.7×	593 0.7×	200 0.7×	155 0.6×	65 0.6×	20	977
Christopher DuBois United States	6	491 0.5×	611 0.7×	147 0.5×	249 0.9×	65 0.6×	10	812
Yanghui Yan China	5	628 0.6×	967 1.1×	233 0.9×	404 1.5×	171 1.6×	6	1.2k
Jinoh Oh South Korea	14	662 0.7×	812 0.9×	133 0.5×	324 1.2×	153 1.4×	30	1.1k

Countries citing papers authored by Leyu Lin

Since Specialization

Citations

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

Fields of papers citing papers by Leyu Lin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Leyu Lin

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

20 of 20 papers shown

1.

Xie, Ruobing, et al.. (2024). Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback. 3645–3656. 6 indexed citations

2.

Hou, Yupeng, Ruobing Xie, Julian McAuley, et al.. (2024). AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems. 3679–3689. 23 indexed citations

3.

Xie, Ruobing, Zhao Zhang, Xu Zhang, et al.. (2024). DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback. 3437–3447. 4 indexed citations

4.

Xie, Ruobing, Yongchun Zhu, Fuzhen Zhuang, et al.. (2024). Personalized Prompt for Sequential Recommendation. IEEE Transactions on Knowledge and Data Engineering. 36(7). 3376–3389. 27 indexed citations

5.

Zhang, Shao‐Liang, Ruobing Xie, Feng Xia, et al.. (2024). CIPPO: Contrastive Imitation Proximal Policy Optimization for Recommendation Based on Reinforcement Learning. IEEE Transactions on Knowledge and Data Engineering. 36(11). 5753–5767. 2 indexed citations

6.

Xie, Ruobing, et al.. (2024). Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach. ACM Transactions on Information Systems. 43(5). 1–37. 39 indexed citations

7.

Ding, Rui, Ruobing Xie, Xiaochun Yang, et al.. (2023). Interpretable User Retention Modeling in Recommendation. 702–708. 3 indexed citations

8.

Xie, Ruobing, et al.. (2023). Triple Sequence Learning for Cross-domain Recommendation. ACM Transactions on Information Systems. 42(4). 1–29. 17 indexed citations

9.

Xie, Ruobing, et al.. (2023). Exploring False Hard Negative Sample in Cross-Domain Recommendation. 502–514. 8 indexed citations

10.

Liang, Xiaobo, et al.. (2023). CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation. 3901–3913. 5 indexed citations

11.

Xia, Feng, et al.. (2023). Graph Exploration Matters: Improving both Individual-Level and System-Level Diversity in WeChat Feed Recommendation. 4901–4908. 1 indexed citations

12.

Xie, Ruobing, et al.. (2023). Generative Next-Basket Recommendation. 737–743. 3 indexed citations

13.

Xie, Ruobing, et al.. (2023). Reweighting Clicks with Dwell Time in Recommendation. 341–345. 2 indexed citations

14.

Yao, Yuan, Bowen Dong, Ao Zhang, et al.. (2022). Prompt Tuning for Discriminative Pre-trained Language Models. Findings of the Association for Computational Linguistics: ACL 2022. 3468–3473. 22 indexed citations

15.

Xie, Ruobing, Shukai Liu, Ziwei Zhang, et al.. (2021). Improving Accuracy and Diversity in Matching of Recommendation With Diversified Preference Network. IEEE Transactions on Big Data. 8(4). 955–967. 29 indexed citations

16.

Xiao, Chaojun, Yuan Yao, Ruobing Xie, et al.. (2020). Denoising Relation Extraction from Document-level Distant Supervision. 3683–3688. 21 indexed citations

17.

Zhang, Zhengyan, Cheng Yang, Zhiyuan Liu, et al.. (2020). COSINE: Compressive Network Embedding on Large-Scale Information Networks. IEEE Transactions on Knowledge and Data Engineering. 34(8). 3655–3668. 3 indexed citations

18.

Su, Yan, et al.. (2020). Learning to Build User-tag Profile in Recommendation System. 2877–2884. 11 indexed citations

19.

Wu, Ruidong, Yuan Yao, Xu Han, et al.. (2019). Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data. 38 indexed citations

20.

Zhu, Hao, Zhiyuan Liu, Ruobing Xie, et al.. (2018). Incorporating Chinese Characters of Words for Lexical Sememe Prediction. 2439–2449. 14 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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