Classic Papers On Recommender System

Here are some classic papers on recommender system that are suitable for beginners.
这里介绍了一些推荐系统经典论文，适合初学者学习使用。

My Google Scholar Profile（Ad）

Here is my Google Scholar profile URL, where you can find some papers related to recommendation systems. If they are helpful to you, feel free to cite them.
求引用，求引用，哈哈哈。

https://scholar.google.com/citations?view_op=list_works&hl=en&hl=en&user=_nuGLhoAAAAJ

Classic Papers

1) Feature Cross

DCN： https://arxiv.org/pdf/1708.05123

FiBiNET: https://arxiv.org/pdf/1905.09433

PEPNet: https://arxiv.org/pdf/2302.01115

2) Retrieval

TDM: https://arxiv.org/pdf/1801.02294

SDM: https://arxiv.org/pdf/1909.00385

MIND： https://arxiv.org/pdf/1904.08030

DSSM： https://posenhuang.github.io/papers/cikm2013_DSSM_fullversion.pdf

YouTubeDNN Retrieval： https://storage.googleapis.com/gweb-research2023-media/pubtools/pdf/6417b9a68bd77033d65e431bdba855563066dc8c.pdf

DR： https://arxiv.org/pdf/2007.07203

3) Ranking

Wide & deep https://arxiv.org/pdf/1606.07792

DeepFM: https://arxiv.org/pdf/1703.04247

BST: https://arxiv.org/pdf/1905.06874v1

YouTubeDNN Ranking： https://dl.acm.org/doi/pdf/10.1145/2959100.2959190

DIN: https://arxiv.org/pdf/1706.06978

DIEN: https://arxiv.org/pdf/1809.03672v1

SIM: https://arxiv.org/pdf/2006.05639

MMOE: https://dl.acm.org/doi/pdf/10.1145/3219819.3220007

4) Re-ranking

MMR： https://www.cs.cmu.edu/~jgc/publication/The_Use_MMR_Diversity_Based_LTMIR_1998.pdf

DPP: https://arxiv.org/pdf/1709.05135

Kuaishou ON-DEVICE RANKING MODEL： https://arxiv.org/pdf/2208.09577

5) Cold Start

POSO: https://arxiv.org/pdf/2108.04690

6) Other

Attention： https://arxiv.org/pdf/1706.03762

Self-supervised Learning： https://arxiv.org/pdf/2007.12865