# 微信「看一看」 朋友在看的增强推荐系统

### 总结

[1] Chen, Chong, et al. “Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation.” Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 01. 2020.

[2] Dong, Yuxiao, Nitesh V. Chawla, and Ananthram Swami. “metapath2vec: Scalable representation learning for heterogeneous networks.” Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. 2017.

[3] Fan, Wenqi, et al. “Graph neural networks for social recommendation.” The World Wide Web Conference. 2019.

[4]Grover, Aditya, and Jure Leskovec. “node2vec: Scalable feature learning for networks.” Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 2016.

[5] Kipf, Thomas N., and Max Welling. “Semi-supervised classification with graph convolutional networks.” arXiv preprint arXiv:1609.02907 (2016).

[6] Perozzi, Bryan, Rami Al-Rfou, and Steven Skiena. “Deepwalk: Online learning of social representations.” Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 2014.

[7] Shi, Chuan, et al. “Heterogeneous information network embedding for recommendation.” IEEE Transactions on Knowledge and Data Engineering 31.2 (2018): 357-370.

[8] Veličković, Petar, et al. “Graph attention networks.” arXiv preprint arXiv:1710.10903 (2017).

[9] Wang, Xiao, et al. “Heterogeneous graph attention network.” The World Wide Web Conference. 2019

[10] Wu, Le, et al. “A neural influence diffusion model for social recommendation.” Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval. 2019.

[11] Xiao, Wenyi, et al. “Beyond personalization: Social content recommendation for creator equality and consumer satisfaction.” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019.

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