Automatic Expert Selection for Multi-Scenario and Multi-Task Search-Reference-Cited by-同舟云学术

Automatic Expert Selection for Multi-Scenario and Multi-Task Search

Published:2022-07-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Zou Xinyu¹,Hu Zhi²,Zhao Yiming²,Ding Xuchu²,Liu Zhongyi²,Li Chenliang¹,Sun Aixin³

Affiliation:

1. Wuhan University, Wuhan, China

2. Ant Group, Hangzhou, China

3. Nanyang Technological University, Singapore, Singapore

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3477495.3531942

Reference28 articles.

1. Rich Caruana . 1997. Multitask learning. Machine learning , Vol. 28 , 1 ( 1997 ), 41--75. Rich Caruana. 1997. Multitask learning. Machine learning, Vol. 28, 1 (1997), 41--75.

2. Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce

3. Self-Supervised Learning on Users' Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce

4. Sepp Hochreiter and Jürgen Schmidhuber . 1997. Long short-term memory. Neural computation , Vol. 9 , 8 ( 1997 ), 1735--1780. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780.

5. Robert A Jacobs , Michael I Jordan , Steven J Nowlan , and Geoffrey E Hinton . 1991. Adaptive mixtures of local experts. Neural computation , Vol. 3 , 1 ( 1991 ), 79--87. Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991. Adaptive mixtures of local experts. Neural computation, Vol. 3, 1 (1991), 79--87.

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