英文期刊论文：
[1] 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: 7.466, JCR-1)
[2] Gan, M.* & Li, D. (2023). A Disaggregated Interest-Extraction Network for Click-Through Rate Prediction. Multimedia Tools and Applications. (SCI)
[3] Xu, J., Gan, M. *, Zhang, X. (2023). MMusic: A hirerarchical multi-information fusion method for deep music recommendation. Journal of Intelligent Information Systems. (SCI)
[4] Gan, M.* & Kwon, O. (2022). A Knowledge-Enhanced Contextual Bandit Approach for Personalized Recommendation in Dynamic Domain. Knowledge-based Systems, 251, 109158. (SCI IF: 8.139, JCR-1)
[5] 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-1)
[6] Gan, M.* & Ma, Y. (2022). Knowledge transfer learning from multiple user activities to improve personalized recommendation. Soft Computing, 26: 6547–65661. (SCI IF: 3.732, JCR-2)
[7] 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, ABS***, JCR-1)
[8] Gan, M.* & Tan, C. (2022). Mining multiple sequential patterns through multi-graph representation for next point-of-interest recommendation. World Wide Web, 1-26. (SCI)
[9] Ren, J. & Gan, M.* (2022). Mining dynamic preferences from geographical and interactive correlations for next POI recommendation.Knowledge and Information Systems, 1-24. (SCI)
[10] 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)
[11] Gan, M.* & Zhang, X. (2021). Integrating Community Interest and Neighbor Semantic for Microblog Recommendation. International Journal of Web Services Research, 18(2), 54-75. (SCI)
[12] Gan, M.* & Cui, H (2021). Exploring User Movie Interest Space: A Deep Learning Based Dynamic Recommendation Model. Expert Systems with Applications. (SCI IF: 5.452, JCR-1)
[13] Zhang, H & Gan，M.* (2020). Incorporating People’s Memory-based Preference and Point-of-Interest Stickiness for Recommendation in Location-based Social Networks. ISPRS International Journal of Geo-information (SCI)
[14] Ma, Y., Gan, M. (2020). Exploring multiple spatio-temporal information for point-of-interest recommendation. Soft Computing 24(24), 18733-18747
[15] Gan M*. & Zhang, X. (2021) Integrating Community Interest and Neighbor Semantic for Microblog Information Recommendation. International Journal of Web Services Research, 18(2), 54-75.（SCI）
[16] 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***)
[17] Chen S, Gan M*, et al (2019). DeepCAPE: a deep convolutional neural network for the accurate prediction of enhancers. Bioinformatics (SCI IF: 6.615, JCR-1)
[18] 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)
[19] 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-2)
[20] 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-2)
[21] 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-2)
[22] 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-2)
[23] Gan M (2016). COUSIN: A network-based regression model for personalized recommendations. Decision Support Systems, 82: 58-68. (SCI IF: 3.565, JCR-2, ABS***)
[24] 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-2)
[25] 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-2)
[26] 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-2)
[27] 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 IF: 0.672)
[28] Gan M (2014). Walking on a User Similarity Network towards Personalized Recommendations. PLoS ONE 9(12): e114662. (SCI IF: 3.234)
中文期刊论文：
[1] 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)
[2] 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）
[3] 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)
[4] 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)
国际会议论文：
[1] 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.
[2] 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)
[3] 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)
[4] Gan M, Ma Y (2018). A Random Forest Regression-based Personalized Recommendation Method. 22th Pacific Asia Conference on Information Systems (PACIS), Japan, 2018.
[5] 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)
[6] 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.
[7] 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）
