Fuyu Lv

874 total citations
10 papers, 194 citations indexed

About

Fuyu Lv is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Fuyu Lv has authored 10 papers receiving a total of 194 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Information Systems, 6 papers in Artificial Intelligence and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Fuyu Lv's work include Recommender Systems and Techniques (7 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Advanced Graph Neural Networks (3 papers). Fuyu Lv is often cited by papers focused on Recommender Systems and Techniques (7 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Advanced Graph Neural Networks (3 papers). Fuyu Lv collaborates with scholars based in China, Hong Kong and Australia. Fuyu Lv's co-authors include Keping Yang, Wilfred Ng, Fei Sun, Quan Lin, Xiaoyi Zeng, Hong Wen, Jing Zhang, Xiao-Ming Wu, Qianli Ma and Sen Li and has published in prestigious journals such as Rare & Special e-Zone (The Hong Kong University of Science and Technology), Proceedings of the ACM Web Conference 2022 and Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.

In The Last Decade

Fuyu Lv

9 papers receiving 181 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fuyu Lv China	7	151	134	53	33	25	10	194
Jinze Bai China	3	154 1.0×	118 0.9×	50 0.9×	37 1.1×	14 0.6×	4	183
Shaoyun Shi China	9	134 0.9×	220 1.6×	37 0.7×	31 0.9×	16 0.6×	15	273
Gabriel de Souza Pereira Moreira United States	7	185 1.2×	144 1.1×	47 0.9×	38 1.2×	14 0.6×	18	220
Teng Xiao China	9	176 1.2×	182 1.4×	47 0.9×	66 2.0×	25 1.0×	17	259
Biao Li China	8	161 1.1×	130 1.0×	56 1.1×	75 2.3×	21 0.8×	13	220
Yiheng Shu China	2	118 0.8×	92 0.7×	28 0.5×	34 1.0×	13 0.5×	3	140
Chenxu Zhu China	7	187 1.2×	155 1.2×	76 1.4×	40 1.2×	20 0.8×	16	246
Kangyi Lin China	6	158 1.0×	160 1.2×	57 1.1×	26 0.8×	11 0.4×	9	207
Chenyi Lei China	9	181 1.2×	175 1.3×	103 1.9×	28 0.8×	22 0.9×	18	266
Deqing Yang China	9	120 0.8×	177 1.3×	35 0.7×	28 0.8×	17 0.7×	34	232

Countries citing papers authored by Fuyu Lv

Since Specialization

Citations

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

Fields of papers citing papers by Fuyu Lv

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fuyu Lv

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

10 of 10 papers shown

1.

Zheng, Xiaoyang, et al.. (2023). Delving into E-Commerce Product Retrieval with Vision-Language Pre-training. 3385–3389. 3 indexed citations

2.

Li, Sen, Fuyu Lv, Yukun Zheng, et al.. (2022). Query Rewriting in TaoBao Search. Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 3262–3271. 3 indexed citations

3.

Xu, Yue, Fuyu Lv, Shengyu Zhang, et al.. (2022). Intelligent Request Strategy Design in Recommender System. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3772–3782. 9 indexed citations

4.

Wen, Hong, et al.. (2022). Re-weighting Negative Samples for Model-Agnostic Matching. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1823–1827.

5.

Wen, Hong, et al.. (2022). Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation. Proceedings of the ACM Web Conference 2022. 422–430. 18 indexed citations

6.

Fan, Lu, Qimai Li, Bo Liu, et al.. (2022). Modeling User Behavior with Graph Convolution for Personalized Product Search. Proceedings of the ACM Web Conference 2022. 203–212. 9 indexed citations

7.

Chen, Jiawei, Wenjun Peng, YE Wen-qin, et al.. (2022). IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search. Proceedings of the ACM Web Conference 2022. 256–265. 13 indexed citations

8.

Wen, Hong, et al.. (2021). Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction. 2187–2191. 18 indexed citations

9.

Li, Sen, Fuyu Lv, Keping Yang, et al.. (2021). Embedding-based Product Retrieval in Taobao Search. 3181–3189. 33 indexed citations

10.

Lv, Fuyu, et al.. (2019). SDM. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 2635–2643. 88 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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