Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning-Reference-Cited by-同舟云学术

Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:The World Wide Web Conference on - WWW '19
language:
Short-container-title:

Author:

Takanobu Ryuichi¹,Zhuang Tao²,Huang Minlie³,Feng Jun⁴,Tang Haihong²,Zheng Bo²

Affiliation:

1. Tsinghua University, Japan

2. Alibaba Group, China

3. Tsinghua University, China

4. State Grid, China

Publisher

ACM Press

Reference45 articles.

1. Jaime Arguello. 2017. Aggregated search. Foundations and Trends in Information Retrieval10, 5 (2017), 365-502.

2. Jaime Arguello, Fernando Diaz, and Jamie Callan. 2011. Learning to aggregate vertical results into web search results. In Proc. 20th ACM Int. Conf. Information and Knowledge Management. 201-210.

3. Jaime Arguello, Fernando Diaz, Jamie Callan, and Ben Carterette. 2011. A methodology for evaluating aggregated search results. In Proc. 33rd European Conf. Information Retrieval. 141-152.

4. Jaime Arguello, Fernando Diaz, Jamie Callan, and Jean-Francois Crespo. 2009. Sources of evidence for vertical selection. In Proc. 32nd Int. ACM SIGIR Conf. Research and Development in Information Retrieval. 315-322.

5. Andrew G Barto and Sridhar Mahadevan. 2003. Recent advances in hierarchical reinforcement learning. Discrete Event Dynamic Systems13 (2003), 341-379.

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