甘明鑫，北京科技大学，永利集团88304官网在线登录，管理科学与工程系，教授，博士生导师。2001年毕业于清华大学自动化系，自动化专业，获工学学士学位，2006年毕业于北京理工大学管理与经济学院，管理科学与工程专业，获管理学博士学位。2006年3月至今，任永利集团88304官网在线登录讲师、副教授、教授。以访问学者身份赴美国加州大学伯克利分校统计系 (2014-2015)、美国南加州大学生物科学系 (2014)、英国里丁大学系统工程系 (2004-2005) 访学交流。

主要研究领域为智能推荐系统、社会媒体分析与计算、智慧医疗与健康管理、大数据挖掘与知识发现。近些年，主持国家自然科学基金青年基金和面上项目 4 项，主持腾讯科技、中国农业科学院等企事业单位多项合作研究课题。以第一作者和通讯作者在Decision Support Systems、Information Processing & Management、Knowledge-based Systems、Information Sciences、Future Generation Computer Systems、Expert Systems with Applications、Information Systems Frontiers、Bioinformatics、Applied Intelligence等国际高水平学术期刊和国际学术会议上总共发表论文80余篇。

学术职务

• 中国信息经济学会理事

• 《管理科学》期刊专题主编

• Program Chair, International Conference for Smart Health 2019

• Min-track Co-chair, Social Media Management in Big Data Era, Hawaii International Conference on System Sciences (HICSS 2019- )

• Decision Support Systems、Information Sciences、Information Systems、Future Generation Computer Systems、Expert Systems with Applications、Information Systems Frontiers、Knowledge-based systems、系统工程理论与实践、中国管理科学等学术期刊审稿人

• 国家自然科学基金管理科学部、信息科学部同行通讯评议专家

• 中国博士后科学基金评审专家

2001.7 清华大学，自动化系，自动化专业，获工学学士学位

2006.3 北京理工大学，管理与经济学院，管理科学与工程专业，获管理学博士学位

1. 2023-2026，国家自然科学基金面上项目：跨领域社会化推荐的因果推断与图表示学习方法，负责人

2. 2021，腾讯犀牛鸟专项研究计划：基于图神经网络的推荐系统算法研究与应用，负责人

3. 2019-2022，国家自然科学基金面上项目：移动场景中用户出行意图预测与地点推荐的智能方法研究，负责人

4. 2019, 中央高校基本科研业务费资助项目：基于主题挖掘与深度学习的移动场景演化分析与预测方法研究，负责人

5. 2015-2018，国家自然科学基金面上项目：大数据环境下融合多源信息的推荐系统关键问题研究，负责人

6. 2018-2021，国家自然科学基金海外及港澳学者合作研究基金重点项目：多终端网络环境下的用户服务理论与方法研究，主要参与人

7. 2017，中央高校基本科研业务费资助项目（精品文科）：大数据环境下复杂系统决策分析的理论与方法，负责人

8. 2012-2014，国家自然科学基金青年基金项目：基于本体与异质复杂网络的推荐系统研究，负责人

9. 2012-2015，国家自然科学基金面上项目：敏捷供应链知识服务网络形成、演化与治理机制研究，参与人

10. 2016-2017，企业科研课题：智能大数据集成分析平台规划设计方案，负责人

11. 2015-2017，中国农业科学院：农业科技信息大数据分析与智能化知识推荐，负责人

12. 2011-2013，中央高校基本科研业务费资助项目：企业信息系统映射与演化的同构理论与应用，负责人

13. 2011-2013，企业科研课题：面向客户需求的电子商务信息推荐系统设计，负责人

14. 2010-2012，企业科研课题：人体识别智能信息系统需求分析与建模，负责人

15. 2009-2010，工业信息化部重点招投标项目子课题：电子政务系统的需求分析方法研究，负责人

2003年 获得中国农业科学院科学技术成果二等奖

2006年 博士毕业论文获得北京理工大学优秀博士论文奖

2011年 专著《电子政务系统的需求分析》获得北京科技大学专著奖

英文期刊论文：

[1] Gan, M.*, Wang, C., Yi, L., & Gu, H. (2024). Exploiting dynamic social feedback for session-based recommendation. Information Processing & Management, 61(3), 103632. (SCI IF=8.6, JCR Q1)

[2] Gan, M.*, & Zhang, H. (2023). VIGA: A variational graph autoencoder model to infer user interest representations for recommendation. Information Sciences, 640, 119039. (SCI IF=8.1, JCR Q1)

[3] Gan, M.*, & Ma, Y. (2023). Mapping user interest into hyper-spherical space: a novel poi recommendation method. Information Processing & Management, 60(2), 103169. (SCI IF=8.6, JCR Q1)

[4] Gan, M.*, Xu, G., & Ma, Y. (2023). A multi-behavior recommendation method exploring the preference differences among various behaviors. Expert Systems with Applications, 228, 120316. (SCI IF=8.5, JCR Q1)

[5] Gan, M.*, & Kwon, O. C. (2022). A knowledge-enhanced contextual bandit approach for personalized recommendation in dynamic domains. Knowledge-Based Systems, 251, 109158. (SCI IF=8.1, JCR Q1)

[6] Hua, S., & Gan, M.* (2023). Intention-aware denoising graph neural network for session-based recommendation. Applied Intelligence, 53(20), 23097-23112. (SCI IF=5.3, JCR Q2)

[7] Pan, X., & Gan, M.* (2023). Multi-behavior recommendation based on intent learning. Multimedia Systems, 29(6), 3655-3668. (SCI IF=3.9, JCR Q1)

[8] Gan, M.*, & Ma, Y. (2022). DeepInteract: Multi-view features interactive learning for sequential recommendation. Expert Systems with Applications, 204, 117305. (SCI IF=8.665, JCR Q1)

[9] Ma, Y., & Gan, M.* (2021). DeepAssociate: A deep learning model exploring sequential influence and history-candidate association for sequence recommendation. Expert Systems with Applications, 185, 115587. (SCI IF=6.954, JCR Q1)

[10] Gan, M.*, & Ma, Y. (2022). Knowledge transfer learning from multiple user activities to improve personalized recommendation. Soft Computing, 26(14), 6547-6566. (SCI IF=3.732, JCR Q2)

[11] Gan, M.*, & Cui, H. (2021). Exploring user movie interest space: A deep learning based dynamic recommendation model. Expert Systems with Applications, 173, 114695. (SCI IF=5.452, JCR Q1)

[12] Gan, M.*, Zhang, X., & Wang, W. (2023). Dual-evolution: a deep sequence learning model exploring dual-side evolutions for movie recommendation. Electronic Commerce Research, 1-29. (SCI)

[13] Gan, M.*, Li, D., & Zhang, X. (2023). A disaggregated interest-extraction network for click-through rate prediction. Multimedia Tools and Applications, 82(18): 27771-27793. (SCI)

[14] Zhang, X., & Gan, M.* (2024). Hi-GNN: hierarchical interactive graph neural networks for auxiliary information-enhanced recommendation. Knowledge and Information Systems, 66(1), 115-145. (SCI)

[15] Ren, J., & Gan, M.* (2023). Mining dynamic preferences from geographical and interactive correlations for next POI recommendation. Knowledge and Information Systems, 65(1), 183-206. (SCI)

[16] Gan, M.*, & Tan, C. (2023). Mining multiple sequential patterns through multi-graph representation for next point-of-interest recommendation. World Wide Web, 26(4), 1345-1370. (SCI)

[17] Xu, J., Gan, M.*, & Zhang, X. (2023). MMusic: a hierarchical multi-information fusion method for deep music recommendation. Journal of Intelligent Information Systems, 61(3): 795-818. (SCI)

[18] Zhang, X., & Gan, M.* (2023). C-GDN: core features activated graph dual-attention network for personalized recommendation. Journal of Intelligent Information Systems, 1-22. (SCI)

[19] Zhang, H., Gan, M.*, & Sun, X. (2021). Incorporating memory-based preferences and point-of-interest stickiness into recommendations in location-based social networks. ISPRS International Journal of Geo-Information, 10(1), 36. (SCI)

[20] Gan, M.*, & Zhang, X. (2021). Integrating Community Interest and Neighbor Semantic for Microblog Recommendation. International Journal of Web Services Research (IJWSR), 18(2), 54-75. (SCI)

[21] Ma, Y., & Gan, M.* (2020). Exploring multiple spatio-temporal information for point-of-interest recommendation. Soft Computing, 24(24), 18733-18747. (SCI)

[22] Gan, M.*, et al. (2019). GLORY: exploring global and local correlations for personalized social recommendations. Information Systems Frontiers, 21(4): 925–939. (SCI IF: 2.539, ABS***)

[23] Chen, S., Gan, M., et al (2019). DeepCAPE: a deep convolutional neural network for the accurate prediction of enhancers. Bioinformatics, 19(4), 565-577. (SCI IF: 6.615, JCR Q1)

[24] Gan, M.*, & Gao, L. (2019). Discovering Memory-Based Preferences for POI Recommendation in Location-Based Social Networks. ISPRS international journal of geo-information, 8(6): 279. (SCI)

[25] Gan, M., & Jiang, R.* (2018). Flower: fusing global and local associations towards personalized social recommendation. Future Generation Computer Systems, 78(1), 462-473. (SCI IF: 6.125, JCR Q2)

[26] Gan, M.*, et al. (2017). Mimvec: a deep learning approach for analyzing the human phenome, BMC Systems Biology, 11(S4): 76. (SCI IF: 2.303, JCR Q2)

[27] Liu, Q., Gan, M., et al. (2017). A sequence-based method to predict the impact of regulatory variants using random forest. BMC Systems Biology, 11(S2), 7. (SCI IF: 2.303, JCR Q2)

[28] Gan, M.* (2016). TAFFY: Incorporating tag information into a diffusion process for personalized recommendations. World Wide Web Journal, 19(5): 933-955. (SCI IF: 1.474, JCR Q2)

[29] Gan, M.* (2016). COUSIN: A network-based regression model for personalized recommendations. Decision Support Systems, 82: 58-68. (SCI IF: 3.565, JCR Q2, ABS***)

[30] Gan, M., & Jiang, R.* (2013). Improving accuracy and diversity of personalized recommendation through power law adjustments of user similarities. Decision Support Systems, 55(3): 811-821. (SCI IF: 3.565, ABS***, JCR Q2)

[31] Gan, M.*, & Jiang, R. (2015). ROUND: Walking on an object-user heterogeneous network for personalized recommendations. Expert Systems with Applications, 42(22), 8791–8804. (SCI IF: 3.928, ABS***, JCR Q2)

[32] Gan, M., & Jiang, R.* (2013). Constructing a user similarity network to remove adverse influence of popular objects for personalized recommendation. Expert Systems with Applications, 40(10): 4044-4053. (SCI IF: 3.928, ABS***, JCR Q2)

[33] Gan, M.* (2016). Trinity: walking on a user-object-tag heterogeneous network for personalized tag-aware recommendation. Journal of Computer Science and Technology, 31(3): 577–594. (SCI)

[34] Gan, M.* (2014). Walking on a User Similarity Network towards Personalized Recommendations. PLoS ONE, 9(12): e114662. (SCI)

中文期刊论文：

[1] 梁雨欣, 甘明鑫*, 张雄涛. 基于多属性感知图神经网络的会话推荐方法. 运筹与管理, 已接收 (CSSCI)

[2] 张雄涛, 甘明鑫*, 李硕. 多粒度关系融合的微博信念网络检索模型. 管理科学, 2023. (CSSCI)

[3] 李丹阳, 甘明鑫*. 基于多源信息融合的音乐推荐方法. 数据分析与知识发现, 2021, 5 (2): 94-105. (CSSCI)

[4] 马莹雪, 甘明鑫*, 肖克峻. 融合标签和内容信息的矩阵分解推荐方法. 数据分析与知识发现, 2021, 5(05): 71-82. (CSSCI)

[5] 张雄涛, 甘明鑫*. 隐私视角下社交媒体推荐对用户在线交互意向的影响机理研究.现代情报, 2021, 41(05): 33-43+103. (CSSCI)

国际国内会议论文：

[1] Kwon, O. C., & Gan, M.* (2023). Calibration using knowledge graph attributes in recommender systems. In Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023) (Vol. 12800, pp. 1307-1311). SPIE.

[2] Wang, C., Gan, M.*, Yi, L., & Gu, H. Enhancing Reinforcement Reasoning with Graph Neural Networks for Recommendation. CNAIS 2023.

[3] 李硕, 甘明鑫*, 易玲玲, 谷皓. 基于去噪对比学习的社会化推荐方法. CNAIS 2023.

[4] Kwon, O., Gan, M.* & Zhang, X. (2021). ILFM: Item Attribute-Aware Latent Factor Model for Personalized Recommendation. 25th Pacific Asia Conference on Information Systems (PACIS), 2021.

[5] Gan, M.*, et al. (2019). CDMF: A Deep Learning Model based on Convolutional and Dense-layer Matrix Factorization for Context-Aware Recommendation. 2019 52th Hawaii International Conference on System Sciences (HICSS), January 8-11, 2019. (Best Paper Nomination) (EI)

[6] Ma, Y., & Gan, M.* (2019). Gradient Boosting based prediction method for patient death in hospital treatment. In Proceedings of the 7th International Conference for Smart Health (ICSH). June, Shezhen, China. (EI)

[7] Gan, M.*, Ma, Y. (2018). A Random Forest Regression-based Personalized Recommendation Method. 22th Pacific Asia Conference on Information Systems (PACIS), Japan, 2018.

[8] Gan, M.*, et al (2018). Does daily travel pattern disclose people’s preference? 2018 51th Hawaii International Conference on System Sciences (HICSS), January 3-7, 2018. (EI)

[9] Gan, M.*, et al (2018). Fusing multi-source information via D-S evidence theory towards personalized recommendation in the big data era. 2018 International Conference on Management and Operations Research (ICMOR), Beijing, China, July 7-9, 2018.

[10] Gan, M.*, Han, Y., & Gao, L. (2017). TRACE: Combination of Real-time Trajectory and Contextual Big Data towards Precise Prediction of People’s Behavioral Intentions. the 1st International Conference on Internet Plus, Big Data & Business Innovation, Beijing, 2017.7.8-2017.7.9.

专著与教材：
[1] 甘明鑫，曹菁. 电子政务系统的需求分析 [M]. 北京: 机械工业出版社. 2011.1 （第一著者）
[2] 甘仞初，甘明鑫，杜晖，颜志军. 信息系统分析设计与管理 [M]. 北京：高等教育出版社. 2009. 10 （第二著者）
[3] Gan Mingxin, Han Botang and Liu Kecheng, “Semiotic transformation from business domain to IT domain in information systems development”, In P. –J. Charrel & D. Galarreta (Eds.), Project Management and Risk Management in Complex Projects-Studies in Organizational Semiotics, Part 4, FR: Springer, 2007.（参编第四部分）

智能推荐系统、社会媒体分析与计算、智慧医疗与智慧健康、大数据挖掘与知识发现

本科生课程（大数据专业）：人工智能，机器学习

本科生课程（信管专业）：决策支持系统，智能方法与技术

研究生课程：人工智能基础与应用，人工智能与大数据前沿方法