Tencent AI Lab 官网
Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse

Millions of conversations are generated every day on social media platforms. With limited attention, it is challenging for users to select which discussions they would like to participate in. Here we propose a new method for microblog conversation recommendation.While much prior work has focused on postlevel recommendation, we exploit both the conversational context,and user content and behavior preferences. We propose a statistical model that jointly captures: (1) topics for representing user interests and conversation content,and (2) discourse modes for describing user replying behavior and conversation dynamics.Experimental results on two Twitter datasets demonstrate that our system outperforms methods that only model content without considering discourse.

NAACL 2018
Publication Time
Xingshan Zeng, Jing Li, Lu Wang, Nicholas Beauchamp, Sarah Shugars, Kam-Fai Wong