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

Haihui Tan, Ziyu Lu, Wenjie Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 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 languageEnglish
Title of host publicationSIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages913-916
Number of pages4
ISBN (Electronic)9781450350228
DOIs
Publication statusPublished - 7 Aug 2017
Event40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 - Tokyo, Shinjuku, Japan
Duration: 7 Aug 201711 Aug 2017

Conference

Conference40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017
Country/TerritoryJapan
CityTokyo, Shinjuku
Period7/08/1711/08/17

Keywords

  • Deep Reinforcement Learning
  • Real-Time Pushing
  • Text Stream

ASJC Scopus subject areas

  • Information Systems
  • Software
  • Computer Graphics and Computer-Aided Design

Cite this