Thread Popularity Prediction and Tracking with a Permutation-invariant Model

Hou Pong Chan, Irwin King


Abstract
The task of thread popularity prediction and tracking aims to recommend a few popular comments to subscribed users when a batch of new comments arrive in a discussion thread. This task has been formulated as a reinforcement learning problem, in which the reward of the agent is the sum of positive responses received by the recommended comments. In this work, we propose a novel approach to tackle this problem. First, we propose a deep neural network architecture to model the expected cumulative reward (Q-value) of a recommendation (action). Unlike the state-of-the-art approach, which treats an action as a sequence, our model uses an attention mechanism to integrate information from a set of comments. Thus, the prediction of Q-value is invariant to the permutation of the comments, which leads to a more consistent agent behavior. Second, we employ a greedy procedure to approximate the action that maximizes the predicted Q-value from a combinatorial action space. Different from the state-of-the-art approach, this procedure does not require an additional pre-trained model to generate candidate actions. Experiments on five real-world datasets show that our approach outperforms the state-of-the-art.
Anthology ID:
D18-1376
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3392–3401
Language:
URL:
https://aclanthology.org/D18-1376
DOI:
10.18653/v1/D18-1376
Bibkey:
Cite (ACL):
Hou Pong Chan and Irwin King. 2018. Thread Popularity Prediction and Tracking with a Permutation-invariant Model. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3392–3401, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Thread Popularity Prediction and Tracking with a Permutation-invariant Model (Chan & King, EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1376.pdf
Attachment:
 D18-1376.Attachment.zip