Neural Network based Reinforcement Learning for Real-time Pushing on Text Stream

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.