Bo Long

806 total citations
21 papers, 506 citations indexed

About

Bo Long is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Bo Long has authored 21 papers receiving a total of 506 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Information Systems, 12 papers in Artificial Intelligence and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Bo Long's work include Recommender Systems and Techniques (13 papers), Topic Modeling (5 papers) and Advanced Bandit Algorithms Research (5 papers). Bo Long is often cited by papers focused on Recommender Systems and Techniques (13 papers), Topic Modeling (5 papers) and Advanced Bandit Algorithms Research (5 papers). Bo Long collaborates with scholars based in United States, China and Canada. Bo Long's co-authors include Bee-Chung Chen, Deepak Agarwal, Shuang-Hong Yang, Hongyuan Zha, Zhaohui Zheng, Alexander J. Smola, Lingfei Wu, Qi Shen, Fangli Xu and Liang Tang and has published in prestigious journals such as AI Magazine, Frontiers in Cardiovascular Medicine and ACM Transactions on Intelligent Systems and Technology.

In The Last Decade

Bo Long

20 papers receiving 490 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bo Long United States	10	381	304	107	106	47	21	506
Hande Dong China	3	271 0.7×	251 0.8×	102 1.0×	74 0.7×	22 0.5×	4	404
Ziwei Fan China	11	437 1.1×	396 1.3×	103 1.0×	99 0.9×	63 1.3×	37	570
Ilya Markov Netherlands	12	366 1.0×	256 0.8×	113 1.1×	118 1.1×	58 1.2×	31	533
Vito Claudio Ostuni Italy	9	416 1.1×	384 1.3×	72 0.7×	114 1.1×	59 1.3×	14	622
P. Dolan United States	3	350 0.9×	214 0.7×	70 0.7×	118 1.1×	62 1.3×	3	446
Balázs Hidasi Hungary	7	419 1.1×	288 0.9×	113 1.1×	148 1.4×	45 1.0×	14	488
Xuezhi Cao China	10	241 0.6×	300 1.0×	71 0.7×	84 0.8×	39 0.8×	21	408
Na Mou China	3	528 1.4×	325 1.1×	106 1.0×	229 2.2×	95 2.0×	7	628
Vreixo Formoso Spain	4	351 0.9×	119 0.4×	71 0.7×	117 1.1×	61 1.3×	13	380
Dawei Yin United States	11	205 0.5×	233 0.8×	33 0.3×	95 0.9×	41 0.9×	17	410

Countries citing papers authored by Bo Long

Since Specialization

Citations

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

Fields of papers citing papers by Bo Long

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bo Long

This figure shows the co-authorship network connecting the top 25 collaborators of Bo Long. A scholar is included among the top collaborators of Bo Long 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 Bo Long. Bo Long 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.

Long, Bo, et al.. (2025). A Scalable Pretraining Framework for Link Prediction with Efficient Adaptation. 2621–2632.

2.

Abdul-Mageed, Muhammad, et al.. (2024). EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations. 1010–1015. 4 indexed citations

3.

Guo, Xiaojie, Shugen Wang, Jiajia Chen, et al.. (2023). Intelligent online selling point extraction and generation for e‐commerce recommendation. AI Magazine. 44(1). 16–29. 4 indexed citations

4.

Wang, Hongning, et al.. (2023). Meta Policy Learning for Cold-Start Conversational Recommendation. 222–230. 23 indexed citations

5.

Dou, Zhicheng, et al.. (2023). Contrastive Learning for User Sequence Representation in Personalized Product Search. 380–389. 4 indexed citations

6.

Shen, Qi, Lingfei Wu, Yiming Zhang, et al.. (2023). Multi-Interest Multi-Round Conversational Recommendation System with Fuzzy Feedback Based User Simulator. 2(4). 1–29. 4 indexed citations

7.

Gong, Juan, Zhenlin Chen, Chaoyi Ma, et al.. (2023). Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce. 3222–3234. 4 indexed citations

8.

Lei, Haike, Mengyang Zhang, Chun Liu, et al.. (2022). Development and Validation of a Risk Prediction Model for Venous Thromboembolism in Lung Cancer Patients Using Machine Learning. Frontiers in Cardiovascular Medicine. 9. 845210–845210. 16 indexed citations

9.

Zhang, Yiming, Lingfei Wu, Qi Shen, et al.. (2022). Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation. Proceedings of the ACM Web Conference 2022. 2153–2162. 35 indexed citations

10.

Deng, Lixi, Jingjing Chen, Houye Ji, et al.. (2022). From Abstract to Details. Proceedings of the 30th ACM International Conference on Multimedia. 258–267. 9 indexed citations

11.

Guo, Xiaojie, Shugen Wang, Jiajia Chen, et al.. (2022). Intelligent Online Selling Point Extraction for E-commerce Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence. 36(11). 12360–12368. 10 indexed citations

12.

Shen, Kai, Lingfei Wu, Siliang Tang, et al.. (2021). Learning to Generate Visual Questions with Noisy Supervision. Neural Information Processing Systems. 34. 4 indexed citations

13.

Zhao, Xiangyu, Haochen Liu, Hui Liu, et al.. (2021). AutoDim: Field-aware Embedding Dimension Searchin Recommender Systems. 3015–3022. 36 indexed citations

14.

Gao, Huiji, Weiwei Guo, Jun Jia, et al.. (2020). Deep Learning for Search and Recommender Systems in Practice. 3515–3516. 6 indexed citations

15.

Kazi, Michaeel, Weiwei Guo, Huiji Gao, & Bo Long. (2020). Incorporating User Feedback into Sequence to Sequence Model Training. 2557–2564. 1 indexed citations

16.

Tang, Liang, Bo Long, Bee-Chung Chen, & Deepak Agarwal. (2016). An Empirical Study on Recommendation with Multiple Types of Feedback. 283–292. 52 indexed citations

17.

Agarwal, Deepak, et al.. (2014). LASER. 173–182. 47 indexed citations

18.

Bian, Jiang, Bo Long, Lihong Li, et al.. (2014). Exploiting User Preference for Online Learning in Web Content Optimization Systems. ACM Transactions on Intelligent Systems and Technology. 5(2). 1–23. 8 indexed citations

19.

Agarwal, Deepak, Bee-Chung Chen, & Bo Long. (2011). Localized factor models for multi-context recommendation. 609–617. 54 indexed citations

20.

Yang, Shuang-Hong, Bo Long, Alexander J. Smola, Hongyuan Zha, & Zhaohui Zheng. (2011). Collaborative competitive filtering. 295–304. 108 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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