Xiangyang Li

22 papers receiving 269 citations

Hit Papers

How Can Recommender Systems Benefit from Large Language M...20242026202520241020304050

Peers

Xiangyang Li
Comparison fields: 5 of 61
  • Artificial Intelligence 195
  • Information Systems 80
  • Molecular Biology 58
  • Management Science and Operations Research 28
  • Computer Vision and Pattern Recognition 23
Replace Dominika Tkaczyk with:
Dominika Tkaczyk Poland
Alex Ratner United States
Marcin Mirończuk Poland
Braden Hancock United States
Zara Nasar Pakistan
Parikshit Sondhi United States
Huiyong Xiao United States
Daya C. Wimalasuriya United States
Lavanya Elluri United States
Samir Tartir United States
Xiangyang Li relative to Dominika Tkaczyk Poland Dominika Tkaczyk's profile →
Citations per field
00.5×2.8×
Dominika Tkaczyk · 1×
Citations per year

Countries citing papers authored by Xiangyang Li

Since Specialization
Citations

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

Fields of papers citing papers by Xiangyang Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiangyang Li

This figure shows the co-authorship network connecting the top 25 collaborators of Xiangyang Li. A scholar is included among the top collaborators of Xiangyang Li 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 Xiangyang Li. Xiangyang Li 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
#WorkIndexed citations
1 0
2 1
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How Can Recommender Systems Benefit from Large Language Models: A Surveybreakdown →
53
6 4
7 8
8 4
9 17
10 3
11 130
12 2
13 1
14 5
15 5
16 3
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Heterogeneous Knowledge Exchange of Important Management Periodicals Selected by Department of Management Sciences of National Science Foundation of China
0
18 5
19 12
20 1

About Xiangyang Li

Xiangyang Li is a scholar working on Artificial Intelligence, Information Systems and Organizational Behavior and Human Resource Management, having authored 24 papers that have together received 279 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Recommender Systems and Techniques (7 papers) and Natural Language Processing Techniques (5 papers). The work is most often cited by research in Artificial Intelligence (195 citations), Health Informatics (5 citations) and Information Systems (80 citations). Xiangyang Li has collaborated with scholars based in China, Hong Kong and Sweden. Frequent co-authors include Xiao‐Hua Zhou, Huan Zhang, Ruiming Tang, Huifeng Guo, Bo Chen, Chenxu Zhu, Weinan Zhang, Yong Yu, Weizhao Wang and Weiwen Liu. Their work appears in journals such as IEEE Journal on Selected Areas in Communications, Information & Management and Journal of Biomedical Informatics.

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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