胡光能 博士 华山菁英教授

导师类型：硕士生导师

性别：男

毕业院校：香港科技大学

学历：博士研究生毕业

在职信息：在岗

所在单位：计算机科学与技术学院

电子邮箱：huguangneng AT xidian DOT edu DOT cn

通信地址：

电子邮箱：njuhgn AT gmail DOT com

办公电话：

办公地点：北校区新科技楼A1306，南校区网安AI-0627

胡光能，博士，副教授，硕士生导师。2013年和2016年在南京大学获得学士学位和硕士学位，2021年在香港科技大学获得博士学位（获香港政府博士奖学金计划(HKPFS)全额资助），专业皆为计算机科学与技术。2022年1月入职西安电子科技大学计算机科学与技术学院，聘为华山菁英教授岗，6月评为硕士研究生指导教师。加入认知与语言计算实验室（Cognition & Language Computing Lab, or CLCL），主要研究领域为自然语言处理、迁移机器学习、深度与图学习。在CCF推荐刊物发表一作论文8篇，包括ACM TKDD、IJCAI、EMNLP/NAACL/EACL、WWW/CIKM/PAKDD等。担任AAAI 2021年会组委会程序工作流主席，并长期担任ICML/NeurIPS/ICLR、AAAI/IJCAI、ACL/EMNLP/NAACL、KDD/WWW、ICWSM/CSCW、IEEE TPAMI等程序委员和审稿人30余次。

Bio: I got PhD from HK Univ. of Sci. & Tech. in late 2021 with HKPFS funded, and BS/MS from Nanjing University in 2013/2016 with National Scholarship awarded, all in computer science. I joined Xidian Univ. in early 2022 as Huashan Elite Professor, where I am Associate Professor and Master Supervisor of Sch. of Comp. Sci. & Tech. 

My research interests are deep transfer learning, natural language processing, recommender systems, and privacy-preserving. I have eight first-author research publications appeared in top conferences including WWW, EMNLP, NAACL, CIKM, and IJCAI, and in top journals including TKDD. Moreover, I have served as reviewer & PC member for top journals (incl. IEEE TPAMI) & conferences (incl. ACL/EMNLP/NAACL, ICML/NeurIPS/ICLR, AAAI/IJCAI, SIGKDD/CSCW/ICWSM) over 30 times.

I am looking for self-motivated master students to work with me. Undergraduates in Xidian Univ. are also higly welcomed. Drop me an email if you are interested!

个人主页@github.io |  谷歌学术  |  DBLP (VPN may needed) 

招生信息：1. 学术型研究方向：软件工程；2. 专业型研究方向：电子信息。对科研和发表论文感兴趣的西电本科生也欢迎联系！

研究方向：1. 认知与语言计算实验室；2. 深度与图学习、迁移机器学习、自然语言处理。

更多信息：

@官网 https://faculty.xidian.edu.cn/hugn/zh_CN/index.htm

@Github https://njuhugn.github.io/ 

会议论文

*=informally co-advised students.

8. Dual Side Deep Context-aware Modulation for Social Recommendation. Bairan Fu*, Wenming Zhang*, Guangneng Hu, Xinyu Dai, Shujian Huang & Jiajun Chen. Proceedings of World Wide Web Conference (TheWebConf), 2021, p.2524–2534.   |   [Revs],   [Official],   [Oral],   [Code+Data]

7. TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation. Guangneng Hu & Qiang Yang. Proceedings of European Chapter of the Association for Computational Linguistics (EACL), 2021, p.734–744.   |   [Revs],   [Official],   [Oral (Slides) (Video1 | Video2)],   [Poster]

6. PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation. Guangneng Hu & Qiang Yang. Findings of the Association for Computational Linguistics: EMNLP (EMNLP Findings), 2020, p.4506–4516.   |   [Revs],   [Official],   [Code+Data]

5. Personalized Neural Embeddings for Collaborative Filtering with Text. Guangneng Hu. Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL), 2019, p.2082–2088.   |   [Revs],   [Official],   [Oral (Slides)]

4. Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text. Guangneng Hu, Yu Zhang & Qiang Yang. Proceedings of World Wide Web Conference (TheWebConf), 2019, p.2822–2829.   |   [Revs],   [Official],   [Code],   [Poster]

3. CoNet: Collaborative Cross Networks for Cross-Domain Recommendation. Guangneng Hu, Yu Zhang & Qiang Yang. Proceedings of ACM International Conference on Information and Knowledge Management (CIKM), 2018, p.667–676.   |   [Revs],   [Official],   [Oral (Slides)],   [Code]

2. Integrating Reviews into Personalized Ranking for Cold Start Recommendation. Guang-Neng Hu & Xin-Yu Dai. Advances in Knowledge Discovery and Data Mining (PAKDD), 2017, p.708-720.   |   [Official],   [Oral]

1. A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews. Guang-Neng Hu, Xin-Yu Dai, Yunya Song, Shujian Huang & Jiajun Chen. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI), 2015, p.1756-1762.   |   [Official],   [Code],   [Oral]

期刊论文

1. Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback. Guang-Neng Hu, Xin-Yu Dai, Fengyu Qiu, Rui Xia, Tao Li, Shujian Huang & Jiajun Chen. ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 12, Issue 2, 2018, Article No.: 23, p.1–30.   |   [Official],   [Code]

学位论文

1. Deep and Adversarial Knowledge Transfer in Recommendation. Guangneng Hu, PhD Thesis, Supervisors: Qiang Yang & Lei Chen. Hong Kong University of Science and Technology, 2021.   |   [Official],   [PQDT-Global]

专著（章节）

pdf	"Chapter: Transfer Learning in Recommender Systems". Weike Pan & Guangneng Hu In Transfer Learning, p.279-288. Qiang Yang, Yu Zhang, Wenyuan Dai & Sinno Pan. Cambridge Univ. Press, UK, 2020.   |   [Chinese Version]

1．自然语言处理
2.  迁移机器学习
3．深度与图学习