曾骏,中共党员,博士,副教授,硕士生导师,数据科学系系主任,中国计算机学会高级会员,CCF服务计算专委委员,CCF软件工程专委委员。2013年获得日本九州大学信息智能工学专业博士学位。研究领域包括大语言模型,移动大数据分析,移动用户轨迹挖掘,推荐系统,机器学习,人工智能等。作为项目负责人承担国家自然科学基金青年项目1项、国家重点研发子课题1项、重庆市面上项目2项、博士后基金项目一等资助1项、留学人员回国科研启动金项目1项,以主研身份参与多项国家重点研发计划项目、国家科技支撑计划项目、国家自然科学基金项目;发表学术论文40余篇。担任多个国际权威期刊和会议评审人。
邮箱:zengjun@cqu.edu.cn
加入我们:
本团队以科学研究为主要方向,学术氛围浓厚,倡导学生个性化发展,鼓励并支持学生参加国内外学术交流。欢迎2024年保研、考研以及对科研感兴趣的本科同学加入我们。
毕业生去向:
2021级:王子威2024(浙大Phd)、钟林2024(哈工大Phd)、陶泓锦2024(申万宏源)
2020级:赵翊竹2023长安(重庆)、朱泓宇2023电信(南京)
2019级:于扬 2022 高德地图(北京)、姚娟 2022(少高) 电信(贵州)
2018级:唐浩然 2021 阿里(杭州)
2017级:何欣 2020 哈啰出行(上海)
2016级:李英华 2019 海康威视(杭州)
2015级:李烽 2018 京东(北京)
在校学生:
2024级:骆思思、郭岳屹、颜诗越
2023级:潘胤辰、李跃、徐艺芸(卓工)、何旭政(卓工)
2022级:刘博、董栩男
学生获奖:
李烽获2018年重庆市优秀硕士毕业生
李英华获2018年国家奖学金(20000元)
李英华获2018IBM奖学金(10000元)
学生参加学术交流:
王子威等3位同学于2022.8赴南京参加中国数字服务大会
于扬等4位同学于2021.5赴西安参加ICSS国际会议
何欣于2019.8赴英国参加CollaborateCom 国际会议(CCF 推荐)
唐浩然于2019.7赴葡萄牙参加SEKE国际会议(CCF 推荐)
李烽于2017.12赴英国参加CollaborateCom 国际会议(CCF 推荐)
李烽于2017.11赴张家界参加ICIOT国际会议
李英华于2017.5赴厦门参加ICSS国际会议
主持科研项目:
[1] 重庆市自然科学基金面上项目,面向资源受限智能网联汽车的移动服务推荐研究,在研
[2] 重庆市留学人员科研启动基金,2021.12-2024.12,在研
[3] 国家重点研发计划课题,区域汽车产业生态圈网络协同制造共享云服务平台研发与应用示范,已结题
[4] 重庆市自然科学基金面上项目,移动环境下融合多源语义轨迹的地点推荐方法研究,已结题
[5] 国家自然科学基金青年项目,移动环境下基于用户行为识别的情境感知服务推荐研究,已结题
[6] 中国博士后科学基金资助项目一等资助,已结题
[7] 教育部留学回国人员科研启动基金,已结题
[8] 中央高校基本科研业务经费,已结题
参研科研项目:
[1] 国家自然科学基金面上项目,61379158,“基于异构服务网络分析的Web服务推荐研究”,已结题
[2] 国家自然科学基金面上项目,61672117,“移动环境下基于异构空间信息网络的社会化服务推荐研究”,已结题
[3] 国家自然科学基金青年项目,61602070,“基于用户生成信息分析和异常群组发现的推荐系统托攻击检测研究”,已结题
编写教材:
[1] 《基于DirectX 11的3D图形程序设计案例教程》,曾骏,高旻,熊庆宇,文俊浩,重庆大学出版社,2015年5月.
[2] 《软件工程实训项目案例IV》,文俊浩,曾骏,熊庆宇,雷跃明,谭会辛,喻国良,重庆大学出版社,2019年1月.
[3] 《软件工程实训项目案例III——C++程序设计篇》,熊庆宇,文俊浩,雷跃明,谭会辛,曾骏,杨正益,重庆大学出版社,2016年4月.
专利:
[1] 发明专利:基于用户偏好、社交信誉度和地理位置的兴趣点推荐方法,2017(已授权)
[2] 发明专利:一种基于用户签到稀疏矩阵的深度学习兴趣点推荐方法,2019(已授权)
[3] 发明专利:一种基于多任务学习的云服务发现方法,2021(已授权)
[4] 发明专利:一种基于神经网络和移动上下文的兴趣点推荐方法,2021(已授权)
[5] 发明专利:一种基于联合神经网络的面向关系挖掘的兴趣点推荐方法,2021(已授权)
[6] 发明专利:一种基于混合教学优化算法的云制造服务组合方法,2020(已授权)
[7] 发明专利:一种基于骑行上下文信息的共享单车流量预测方法,2020(已授权)
[8] 发明专利:基于知识图谱和相似度网络的Web服务发现方法, 2020(已授权)
[9] 发明专利:一种基于神经网络和地理影响的兴趣点推荐方法,2020(已授权)
[10] 发明专利:一种基于区域划分和上下文影响的兴趣点推荐方法,2020(已授权)
[11] 发明专利: 一种用于移动推荐系统隐私保护的动态联邦学习方法, 2023.11.21, (专利申请号:202311554840.8)
[12] 发明专利:一种面向移动推荐的基于语义的联邦学习方法, 2023.11.29, (专利申请号:202311607960.X)
[13] 发明专利:一种基于深度语义提取和扩散模型的位置服务推荐方法, 2023.11.30, (专利申请号:202311617266.6)
[14] 发明专利:一种基于跨序列位置解耦表征的兴趣点推荐方法,2023.05.22, (专利申请号:202310577915.8)
[15] 发明专利:一种移动环境下基于时空一致性的联邦学习推荐方法,2023. 05.18, (专利申请号:202310559909.X)
[16] 发明专利:一种基于深度语义提取的兴趣点推荐方法,2023.04.21, (专利申请号:202310431484.4)
[17] 发明专利: 一种基于提示与对比学习的极少样本关系提取方法,2022.10 (专利申请号:2022112618194)
[18] 发明专利: 一种基于时空感知并结合局部和全局偏好的POI预测方法, 2022.09(专利申请号:202211176462.X)
[19] 发明专利: 一种基于句法对比学习的稠密检索方法,2022.09.(专利申请号:202211087538.1)
[20]发明专利:一种基于双流注意力和位置残差连接的文本摘要自动抽取方法,2022(专利申请号:202210950607.0)
[21]发明专利:一种基于动态多掩码和增强对抗的文本匹配方法,2022(专利申请号:202210806846.9)
[22]发明专利:一种基于多视角对比学习的检索方法,2022(专利申请号:202210578261.6)
[23] 发明专利:一种基于多策略深度强化学习的云制造服务组合方法,2021(专利申请号:202111589813.5)
[24] 发明专利:一种基于mask和双向模型的缺失POI轨迹补全方法,2021(专利申请号:202111299422.X)
代表性论文
[1] Bo Liu, Jun Zeng*, Junhao Wen, Min Gao, Wei Zhou. CBRec: A causal way balancing multidimensional attraction effect in POI recommendations,Knowledge-Based Systems,305(2024), 112607. 1-15. https://doi.org/10.1016/j.knosys.2024.112607.(JCR-1, 中科院一区, IF =8.153)
[2] Jun Zeng*, Hongjin Tao, Haoran Tang, Junhao Wen and Min Gao, Global and Local Hypergraph Learning Method with Semantic Enhancement for POI Recommendation. Information Processing and Management, 62(2025), pp. 1-17. https://doi.org/10.1016/j.ipm.2024.103868 (JCR-1, 中科院一区, CCF-B, IF=7.4)
[3] Xunan Dong, Jun Zeng*, Junhao Wen, Min Gao, Wei Zhou, SFL: A Semantic-based Federated Learning Method for POI Recommendation, Information Sciences. 2024(679), pp.1-15. https://doi.org/10.1016/j.ins.2024.121057(JCR-1, 中科院一区, CCF-B, IF =8.1)
[4] Lin Zhong, Jun Zeng*, Ziwei Wang, Wei Zhou, Junhao Wen. SCFL: Spatio-temporal consistency federated learning for next POI recommendation. Information Processing and Management, 61(2024), pp. 1-18. https://doi.org/10.1016/j.ipm.2024.103852(JCR-1, 中科院一区, CCF-B, IF=7.4)
[5] Ziwei Wang, Jun Zeng*, Lin Zhong, Ling Liu, Min Gaoa and Junhao Wen. DSDRec: Next POI recommendation using deep semantic extraction and diffusion model, Information Sciences. 2024 (678), pp.1-20. https://doi.org/10.1016/j.ins.2024.121004 (JCR-1, 中科院一区, CCF-B, IF =8.1)
[6] Hongjin Tao, Jun Zeng*, Ziwei Wang, Lin Zhong, Min Gao, Junhao Wen, Next POI Recommendation Based on Spatial and Temporal Disentanglement Representation, 2023 IEEE International Conference on Web Services (ICWS 2023), July 2 - July 8, 2023,Hybrid, Chicago, IL, United states, pp. 84-90, 2023 (CCF-B )
[7] Ziwei Wang, Jun Zeng*, Hongjin Tao, Lin Zhong. RBPSum: An Extractive Summarization Approach Using Bi-Stream Attention and Position Residual Connection. 2023 International Joint Conference on Neural Networks (IJCNN), June 18 - June 23, 2023, Gold Coast, QLD, Australia, pp. 1-8, 2023.(CCF-C)
[8] Hongjin Tao, Jun Zeng*, Ziwei Wang, Yang Yu, Xiaolin Hu. SynC: A Dense Retrieval Method Based on Syntactical Contrastive Learnin. 2023 International Joint Conference on Neural Networks (IJCNN), June 18 - June 23, 2023, Gold Coast, QLD, Australia, pp. 1-8, 2023.(CCF-C)
[9] Ziwei Wang, Jun Zeng*, Junhao Wen, Min Gao and Wei Zhou, Point-of-interest Recommendation using Deep Semantic Model, Expert Systems with Applications. 2023, DOI: 10.1016/j.eswa.2023.120727(SCI JCR-1, 中科院一区, IF =8.093)
[10] Yang Yu, Jun Zeng*, Lin Zhong, Min Gao, Junhao Wen, Yingbo Wu, Multi-views Contrastive Learning for Dense Text Retrieval, Knowledge-Based Systems, 2023(274), pp.1-10 DOI: 10.1016/j.knosys.2023.110624 (SCI JCR-1, 中科院一区, IF =8.153)
[11]Jun Zeng*, Yizhu Zhao, Ziwei Wang, Hongjin Tao, Min Gao, Junhao Wen,LGSA: A next POI prediction method by using local and global interest with spatiotemporal awareness,Expert Systems with Applications. 2023, DOI:10.1016/j.eswa.2023.120291 (SCI, JCR-1, 中科院一区, IF =8.093)
[12]Jun Zeng*, Yang Yu, Junhao Wen, Wenying Jiang and Luxi Cheng, Personalized Dynamic Attention Multi-task Learning Model for Document Retrieval and Query Generation, Expert Systems with Applications, 213(2023), PP. 1-8. DOI10.1016/j.eswa.2022.119026 (SCI, JCR-1, 中科院一区, IF =8.093)
[13]Jun Zeng*, Juan Yao, Min Gao and Junhao Wen, A service composition method using improved hybrid teaching learning optimization algorithm in cloud manufacturing, Journal of Cloud Computing-Advances Systems and Applications, 2022,11(1),pp.1-14 DOI10.1186/s13677-022-00343-0 (SCI, JCR-2, IF=3.895)
[14]Lin Zhong, Jun Zeng*, Yang Yu, Hongjin Tao, Wenying Jiang and Luxi Cheng, A text matching model based on dynamic multi‐mask and augmented adversarial, Expert Systems, 2022, 40(2), PP. 1-16. DOI10.1111/exsy.13165. (SCI,JCR-2, IF= 2.812)
[15]Jun Zeng*, Haoran Tang, Yizhu Zhao, Junhao Wen, Neu-PCM: Neural-based potential correlation mining for POI recommendation, Applied Intelligence, online access. 2022. DOI:10.1007/s10489-022-04057-3(SCI, JCR-2, IF=5.019)
[16]Jun Zeng*, Yizhu Zhao, Yang Yu, Min Gao, Wei Zhou, Junhao Wen, BMAM: Complete the missing POI in the incomplete trajectory via masked and bidirectional model, EURASIP Journal on Wireless Communications and Networking, 2022:1, pp.1-17.(SCI,JCR-3, IF=2.559)
[17]Hongyu Zhu, Jun Zeng*, Yang Yu and Yingbo Wu, A Zero-Shot Relation Extraction Approach Based on Contrast Learning, 34th International Conference on Software Engineering and Knowledge Engineering (SEKE 2022), July 1, 2022 - July 10, 2022, Pittsburgh, PA, United states, pp. 293-299. (CCF-C)
[18]Jun Zeng*, Haoran Tang, Min Gao and Junhao Wen, “PR-RCUC: A POI Recommendation Model Using Region-Based Collaborative Filtering and User-Based Mobile Context”, Mobile Networks and Applications, 2021, 26 (6) , pp.2434-2444. (SCI,JCR-2, IF=3.077)
[19]Jun Zeng*, Yizhu Zhao, Yang Yu, Min Gao and Wei Zhou, The missing POI completion based on bidirectional masked trajectory model,Collaborative Computing: Networking, Applications and Worksharing - 17th EAI International Conference CollaborateCom 2021 COLLABORATECOM(2021), Oct. 16- 18, 2021,Virtual, Online, pp. 229-243.(CCF-C)
[20]Jun Zeng*, Juan Yao, Yang Yu and Yingbo Wu, Multi-D3QN: A Multi-Strategy Deep Reinforcement Learning for Service Composition in Cloud Manufacturing, Collaborative Computing: Networking, Applications and Worksharing - 17th EAI International Conference CollaborateCom 2021 COLLABORATECOM(2021), Oct. 16- 18, 2021,Virtual, Online, pp. 225-240.(CCF-C)
[21]Juan Yao, Jun Zeng*, Junhao Wen, Wei Zhou and Min Gao, “Hybrid-TC: A Hybrid Teaching-Learning-Based Optimization Algorithm for Service Composition in Cloud Manufacturing”, International Joint Conference on Neural Networks(IJCNN2021), July 18 - 22, 2021,Virtual, Shenzhen, China, pp.1-8.(CCF C类)
[22]姚娟,邢镔,曾骏,文俊浩,云制造服务组合研究综述,计算机科学, Vol. 48, No. 7, July 2021 (中文CCF-B)
[23]于扬,邢镔,曾骏,文俊浩,KSN:一种基于知识图谱和相似度网络的 Web 服务发现方法,计算机科学,Vol. 48, No. 10, 2021, pp. 160-166 (中文CCF-B)
[24]Jun Zeng*, Haoran Tang and Xin He, “RCFC: A Region-Based POI Recommendation Model with Collaborative Filtering and User Context”, Collaborative Computing: Networking, Applications and Worksharing - 16th EAI International Conference CollaborateCom 2020 COLLABORATECOM(2020), October 16-18, Shanghai, China, 2020, pp.656-670(CCF-C)
[25]Jun Zeng*, Xin He, Haoran Tang and Junhao Wen, Predicting the next location: A self‐attention and recurrent neural network model with temporal context, Transaction on Emerging Telecommunications Technologies, 2020,DOI: 10.1002/ett.3898(SCI, JCR-2, IF=3.31)
[26]唐浩然,曾骏,李烽,文俊浩,结合地点类别和社交网络的兴趣点推荐,重庆大学学报, Vol. 43, No. 7, Jul. 2020. (CSCD核心)
[27]Jun Zeng*, Feng Li, Xin He and Junhao Wen, “Fused collaborative filtering with user preference, geographical and social influence for point of interest recommendation”, International Journal of Web Services Research, Vol.16(4), 2019,PP. 40-52. DOI: 10.4018/IJWSR.2019100103 (SCI)
[28]Jun Zeng*, Xin He, Haoran Tang, Junhao Wen , “A next location predicting approach based on a recurrent neural network and self-attention”, Collaborative Computing: Networking, Applications and Worksharing - 15th EAI International Conference CollaborateCom 2019 COLLABORATECOM(2019), August 19- 22, 2019,London, United kingdom,2019: 309-322. ( CCF-C)
[29] Jun Zeng*,Haoran Tang,Yinghua Li and Xin He, “A deep learning model based on sparse matrix for point-of-interest recommendation”, 31st International Conference on Software Engineering and Knowledge Engineering (SEKE 2019), Lisbon, Portugal, July 10-12, 2019, pp. 379-384. DOI: 10.18293/SEKE2019-156 (EI, CCF-C)
[30] Jun Zeng*, Xin He, Feng Li, Yinghua Li, Junhao Wen and Wei Zhou, “A Point-of-Interest Recommendation Method Using User Similarity,” WEB INTELLIGENCE, 16(2), 105-112. 2018.DOI: 10.3233/WEB-180376 (CCF -C)
[31]Jun Zeng*, Feng Li, Junhao Wen and Yingbo Wu, “A Point of Interest Recommendation Approach by Fusing Geographical and Reputation Influence on Location Based Social Networks,” 3th International Conference on Collaborative Computing: Networking, Applications and Worksharing(CollaborateCom 2017), Vol. 252 , pp. 232-242, 2018 (CCF-C)
[32] Jun Zeng*, Feng Li, Yinghua Li, Junhao Wen, Yingbo Wu. “Exploring the Influence of Contexts for Mobile Recommendation,” International Journal of Web Services Research, 14(4), 33-49, 2017, DOI: 10.4018/IJWSR.2017100102 (SCI)