Candidate Matching

A curated list of candidate item matching models

Collaborative Filtering

2021	SimpleX [2] CIKM’21 Huawei
2020	ENMF [3] TOIS’20
2019	EASE^R [4] WWW’19 Netflix	MacridVAE [5] NeurIPS’19 Alibaba
2018	MultVAE [6] WWW’18 Netflix, Google	CMN [7] SIGIR’18 Google
2017	NeuMF [8] WWW’17	CML [9] WWW’17
2011	SLIM [10] ICDM’11
2009	BPR [11] UAI’09

Two-Tower Matching

2020	EBR [12] KDD’20 Facebook
2016	YoutubeDNN [1] RecSys’16 Google
2014	DSSM [13] WWW’14 Microsoft

Multi-Interest Matching

2021	SINE [14] WSDM’21 Alibaba
2020	ComiRec [15] KDD’20 Alibaba	PinnerSage [16] KDD’20 Pinterest
2019	MIND [17] CIKM’19 Alibaba

Sequential Recommendation

2019	BERT4Rec [18] CIKM’19 Alibaba	SDM [19] CIKM’19 Alibaba
2018	SASRec [20] ICDM’18
2016	GRU4Rec [21] ICLR’16 Gravity

Graph-based Matching

2021	UltraGCN [22] CIKM’21 Huawei	SGL [23] SIGIR’21	GF-CF [24] CIKM’21
2020	LightGCN [25] SIGIR’20
2019	NGCF [26] SIGIR’19
2018	PinSage [27] KDD’18 Pinterest	EGES [28] KDD’18 Alibaba

Item-to-Item Matching

2016	Item2Vec [29] MLSP’16 Microsoft
2001	ItemCF [30] WWW’01 GroupLens

Generative Retrieval

2019	JTM [31] NeurIPS’19 Alibaba
2018	TDM [32] KDD’18 Alibaba

Lookalike Models

2019	RALM [33] KDD’19 Tencent

Pretraining

2023	VQRec [34] WWW’23
2022	UniSRec [35] KDD’22

References

1

Paul Covington, Jay Adams, and Emre Sargin. Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16), 191–198. 2016.

2

Kelong Mao, Jieming Zhu, Jinpeng Wang, Quanyu Dai, Zhenhua Dong, Xi Xiao, and Xiuqiang He. SimpleX: A Simple and Strong Baseline for Collaborative Filtering. In The 30th ACM International Conference on Information and Knowledge Management (CIKM '21), 1243–1252. 2021.

3

Chong Chen, Min Zhang, Yongfeng Zhang, Yiqun Liu, and Shaoping Ma. Efficient Neural Matrix Factorization without Sampling for Recommendation. ACM Trans. Inf. Syst., 38(2):14:1–14:28, 2020.

4

Harald Steck. Embarrassingly Shallow Autoencoders for Sparse Data. In The World Wide Web Conference (WWW '19), 3251–3257. 2019.

5

Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu. Learning Disentangled Representations for Recommendation. In Annual Conference on Neural Information Processing Systems (NeurIPS '19), 5712–5723. 2019.

6

Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. Variational Autoencoders for Collaborative Filtering. In Proceedings of the 2018 World Wide Web Conference on World Wide Web (WWW '18), 689–698. 2018.

7

Travis Ebesu, Bin Shen, and Yi Fang. Collaborative Memory Network for Recommendation Systems. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR '18), 515–524. 2018.

8

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW '17), 173–182. 2017.

9

Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge J. Belongie, and Deborah Estrin. Collaborative Metric Learning. In Proceedings of the 26th International Conference on World Wide Web (WWW '17), 193–201. 2017.

10

Xia Ning and George Karypis. SLIM: Sparse Linear Methods for Top-N Recommender Systems. In Proceedings of the 11th IEEE International Conference on Data Mining (ICDM '11), 497–506. 2011.

11

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI '09), 452–461. 2009.

12

Jui-Ting Huang, Ashish Sharma, Shuying Sun, Li Xia, David Zhang, Philip Pronin, Janani Padmanabhan, Giuseppe Ottaviano, and Linjun Yang. Embedding-based Retrieval in Facebook Search. In The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '20), 2553–2561. 2020.

13

Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. Learning Semantic Representations Using Convolutional Neural Networks for Web Search. In Proceedings of the 23rd International World Wide Web Conference (WWW '14), 373–374. 2014.

14

Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, and Xia Hu. Sparse-Interest Network for Sequential Recommendation. In The Fourteenth ACM International Conference on Web Search and Data Mining (WSDM '21), 598–606. 2021.

15

Yukuo Cen, Jianwei Zhang, Xu Zou, Chang Zhou, Hongxia Yang, and Jie Tang. Controllable Multi-Interest Framework for Recommendation. In The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '20), 2942–2951. 2020.

16

Aditya Pal, Chantat Eksombatchai, Yitong Zhou, Bo Zhao, Charles Rosenberg, and Jure Leskovec. PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest. In The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '20), 2311–2320. 2020.

17

Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee. Multi-Interest Network with Dynamic Routing for Recommendation at Tmall. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM '19), 2615–2623. 2019.

18

Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM '19), 1441–1450. 2019.

19

Fuyu Lv, Taiwei Jin, Changlong Yu, Fei Sun, Quan Lin, Keping Yang, and Wilfred Ng. SDM: Sequential Deep Matching Model for Online Large-scale Recommender System. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM '19), 2635–2643. 2019.

20

Wang-Cheng Kang and Julian J. McAuley. Self-Attentive Sequential Recommendation. In IEEE International Conference on Data Mining (ICDM '18), 197–206. 2018.

21

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. Session-based Recommendations with Recurrent Neural Networks. In Proceedings of the 4th International Conference on Learning Representations (ICLR '16). 2016.

22

Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, and Xiuqiang He. UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation. In The 30th ACM International Conference on Information and Knowledge Management (CIKM '21), 1253–1262. 2021.

23

Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. Self-supervised Graph Learning for Recommendation. In The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21), 726–735. 2021.

24

Yifei Shen, Yongji Wu, Yao Zhang, Caihua Shan, Jun Zhang, Khaled B. Letaief, and Dongsheng Li. How Powerful is Graph Convolution for Recommendation? In The 30th ACM International Conference on Information and Knowledge Management (CIKM '21), 1619–1629. 2021.

25

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '20), 639–648. 2020.

26

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. Neural Graph Collaborative Filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '19), 165–174. 2019.

27

Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18), 974–983. 2018.

28

Jizhe Wang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, and Dik Lun Lee. Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18), 839–848. 2018.

29

Oren Barkan and Noam Koenigstein. Item2Vec: Neural Item Embedding for Collaborative Filtering. In The 26th IEEE International Workshop on Machine Learning for Signal Processing (MLSP '16), 1–6. 2016.

30

Badrul Munir Sarwar, George Karypis, Joseph A. Konstan, and John Riedl. Item-based Collaborative Filtering Recommendation Algorithms. In Proceedings of the Tenth International World Wide Web Conference (WWW '01), 285–295. 2001.

31

Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li, Jie He, Han Li, Jian Xu, and Kun Gai. Joint Optimization of Tree-based Index and Deep Model for Recommender Systems. In Annual Conference on Neural Information Processing Systems (NeurIPS '19), 3973–3982. 2019.

32

Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, and Kun Gai. Learning Tree-based Deep Model for Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18), 1079–1088. 2018.

33

Yudan Liu, Kaikai Ge, Xu Zhang, and Leyu Lin. Real-time Attention Based Look-alike Model for Recommender System. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19), 2765–2773. 2019.

34

Yupeng Hou, Zhankui He, Julian J. McAuley, and Wayne Xin Zhao. Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders. In Proceedings of the ACM Web Conference (WWW '23), 1162–1171. 2023.

35

Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, and Ji-Rong Wen. Towards Universal Sequence Representation Learning for Recommender Systems. In The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), 585–593. 2022.