Neural network based reinforcement learning for real-Time pushing on text stream

Haihui Tan; Ziyu Lu; Wenjie Li

doi:10.1145/3077136.3080677

Neural network based reinforcement learning for real-Time pushing on text stream

Haihui Tan, Ziyu Lu, Wenjie Li

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

14 Citations (Scopus)

Abstract

The massive amount of noisy and redundant information in text streams makes it a challenge for users to acquire timely and relevant information in social media. Real-Time notification pushing on text stream is of practical importance. In this paper, we formulate the real-Time pushing on text stream as a sequential decision making problem and propose a Neural Network based Reinforcement Learning (NNRL) algorithm for real-Time decision making, e.g., push or skip the incoming text, with considering both history dependencies and future uncertainty. A novel Q-Network which contains a Long Short Term Memory (LSTM) layer and three fully connected neural network layers is designed to maximize the long-Term rewards. Experiment results on the real data from TREC 2016 Real-Time Summarization track show that our algorithm significantly outperforms state-of-The-Art methods.

Original language	English
Title of host publication	SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher	Association for Computing Machinery, Inc
Pages	913-916
Number of pages	4
ISBN (Electronic)	9781450350228
DOIs	https://doi.org/10.1145/3077136.3080677
Publication status	Published - 7 Aug 2017
Event	40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 - Tokyo, Shinjuku, Japan Duration: 7 Aug 2017 → 11 Aug 2017

Conference

Conference	40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017
Country/Territory	Japan
City	Tokyo, Shinjuku
Period	7/08/17 → 11/08/17

Keywords

Deep Reinforcement Learning
Real-Time Pushing
Text Stream

ASJC Scopus subject areas

Information Systems
Software
Computer Graphics and Computer-Aided Design

More information

10.1145/3077136.3080677

Cite this

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Tan, H, Lu, Z & Li, W 2017, Neural network based reinforcement learning for real-Time pushing on text stream. in SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, Inc, pp. 913-916, 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017, Tokyo, Shinjuku, Japan, 7/08/17. https://doi.org/10.1145/3077136.3080677

Neural network based reinforcement learning for real-Time pushing on text stream. / Tan, Haihui; Lu, Ziyu; Li, Wenjie.
SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, Inc, 2017. p. 913-916.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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T1 - Neural network based reinforcement learning for real-Time pushing on text stream

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AU - Lu, Ziyu

AU - Li, Wenjie

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N2 - The massive amount of noisy and redundant information in text streams makes it a challenge for users to acquire timely and relevant information in social media. Real-Time notification pushing on text stream is of practical importance. In this paper, we formulate the real-Time pushing on text stream as a sequential decision making problem and propose a Neural Network based Reinforcement Learning (NNRL) algorithm for real-Time decision making, e.g., push or skip the incoming text, with considering both history dependencies and future uncertainty. A novel Q-Network which contains a Long Short Term Memory (LSTM) layer and three fully connected neural network layers is designed to maximize the long-Term rewards. Experiment results on the real data from TREC 2016 Real-Time Summarization track show that our algorithm significantly outperforms state-of-The-Art methods.

AB - The massive amount of noisy and redundant information in text streams makes it a challenge for users to acquire timely and relevant information in social media. Real-Time notification pushing on text stream is of practical importance. In this paper, we formulate the real-Time pushing on text stream as a sequential decision making problem and propose a Neural Network based Reinforcement Learning (NNRL) algorithm for real-Time decision making, e.g., push or skip the incoming text, with considering both history dependencies and future uncertainty. A novel Q-Network which contains a Long Short Term Memory (LSTM) layer and three fully connected neural network layers is designed to maximize the long-Term rewards. Experiment results on the real data from TREC 2016 Real-Time Summarization track show that our algorithm significantly outperforms state-of-The-Art methods.

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Neural network based reinforcement learning for real-Time pushing on text stream

Abstract

Conference

Keywords

ASJC Scopus subject areas

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