A Contrastive Sharing Model for Multi-Task Recommendation

Author:

Bai Ting1,Xiao Yudong2,Wu Bin1,Yang Guojun2,Yu Hongyong2,Nie Jian-Yun3

Affiliation:

1. Beijing University of Posts and Telecommunications, China and Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, China

2. Tencent, China

3. University of Montreal, Canada

Funder

The NSFC-General Technology Basic Research Joint Funds

The National Natural Science Foundation of China

NSERC discovery grant of Canada

Publisher

ACM

Reference46 articles.

1. Sanjeev Arora Hrishikesh Khandeparkar Mikhail Khodak Orestis Plevrakis and Nikunj Saunshi. 2019. A theoretical analysis of contrastive unsupervised representation learning. arXiv preprint arXiv:1902.09229(2019). Sanjeev Arora Hrishikesh Khandeparkar Mikhail Khodak Orestis Plevrakis and Nikunj Saunshi. 2019. A theoretical analysis of contrastive unsupervised representation learning. arXiv preprint arXiv:1902.09229(2019).

2. Arthur Asuncion and David Newman. 2007. UCI machine learning repository. (2007). Arthur Asuncion and David Newman. 2007. UCI machine learning repository. (2007).

3. A Neural Collaborative Filtering Model with Interaction-based Neighborhood

4. CTRec

5. Rich Caruana . 1997. Multitask learning. Machine learning 28, 1 ( 1997 ), 41–75. Rich Caruana. 1997. Multitask learning. Machine learning 28, 1 (1997), 41–75.

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