2019年11月
关于推荐系统的论文
- Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi. Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random, The 36th International Conference on Machine Learning (ICML) 2019.
- Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi. KDGAN: Knowledge Distillation with Generative Adversarial Networks, 32nd Conference on Neural Information Processing Systems (NIPS) 2018.
- Yu Sun, Nicholas Jing Yuan, Xing Xie, Kieran McDonald, Rui Zhang. Collaborative Intent Prediction with Real-Time Contextual Data, ACM Transactions on Information Systems (TOIS), 35 (4), 30, 2017.
关于python编程的书籍
1.《编写高质量代码 改善Python程序的91个建议》（未读完）
2019年9月
关于NLP和机器学习的论文
- Cook, P., & Stevenson, S. (2007). Automagically inferring the source words of lexical blends. In Proceedings of the Tenth Conference of the Pacific Association for Computational Linguistics (PACLING-2007) (pp. 289-297).
- Giyatmi, G., Wijayava, R., & Arumi, S. (2017). Blending Words Found In Social Media. JURNAL ARBITRER, 4(2), 65-75.
- Cook, P. (2012). Using social media to find English lexical blends. In Proc. of EURALEX (pp. 846-854).
- Chi, L., Lim, K. H., Alam, N., & Butler, C. J. (2016, December). Geolocation prediction in Twitter using location indicative words and textual features. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT) (pp. 227-234).
- Eisenstein, J., O'Connor, B., Smith, N. A., & Xing, E. P. (2010, October). A latent variable model for geographic lexical variation. In Proceedings of the 2010 conference on empirical methods in natural language processing (pp. 1277-1287). Association for Computational Linguistics.
- Jing, L. P., Huang, H. K., & Shi, H. B. (2002, November). Improved feature selection approach TFIDF in text mining. In Proceedings. International Conference on Machine Learning and Cybernetics (Vol. 2, pp. 944-946). IEEE.
- Rahimi, A., Cohn, T., & Baldwin, T. (2018). Semi-supervised user geolocation via graph convolutional networks. arXiv preprint arXiv:1804.08049.
- Cheng, Z., Caverlee, J., & Lee, K. (2010, October). You are where you tweet: a content-based approach to geo-locating twitter users. In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 759-768). ACM.