题目：推荐算法

主讲人：余卓航

地点：工程学院513

时间：2021-11-29 周一晚上8点

简介：1）推荐算法代码流程图和进展

2）推荐算法数据集

3）传统的推荐算法模型

4）深度学习推荐算法模型

5）推荐算法相关论文总结

6）推荐算法最新研究方法介绍

参考文献：

[1]     Xi, Wu-Dong, et al. "BPAM: Recommendation Based on BP Neural Network with Attention Mechanism." IJCAI. 2019.

[2]     Wu, Qiong, et al. "PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation." IJCAI. Vol. 19. 2019.

[3]     Zheng, Lei, Vahid Noroozi, and Philip S. Yu. "Joint deep modeling of users and items using reviews for recommendation." Proceedings of the tenth ACM international conference on web search and data mining. 2017.

[4]     Chen, Liang, et al. "Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network." IJCAI. 2019.

[5]     Wu, Chuhan, et al. "Neural news recommendation with attentive multi-view learning." arXiv preprint arXiv:1907.05576 (2019).

[6]     Jiang, Junyang, et al. "Convolutional gaussian embeddings for personalized recommendation with uncertainty." arXiv preprint arXiv:2006.10932 (2020).

[7]     Zhou, Xiao, et al. "Collaborative metric learning with memory network for multi-relational recommender systems." arXiv preprint arXiv:1906.09882 (2019).

[8]     Yu, Zeping, et al. "Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation." IJCAI. 2019.

[9]     Xin, Xin, et al. "CFM: Convolutional Factorization Machines for Context-Aware Recommendation." IJCAI. Vol. 19. 2019.

[10] Zhang, Shuai, et al. "Deep learning based recommender system: A survey and new perspectives." ACM Computing Surveys (CSUR) 52.1 (2019): 1-38.

[11] Finn, Chelsea, Pieter Abbeel, and Sergey Levine. "Model-agnostic meta-learning for fast adaptation of deep networks." International Conference on Machine Learning. PMLR, 2017.

[12] Lee, Hoyeop, et al. "Melu: Meta-learned user preference estimator for cold-start recommendation." Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019.

[13] Peng, Danni, et al. "Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems." Fifteenth ACM Conference on Recommender Systems. 2021.