Abstract: 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 post-level 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.
Authors: Xingshan Zeng, Jing Li, Lu Wang, Nicholas Beauchamp, Sarah Shugars, Kam-Fai Wong (The Chinese University of Hong Kong, Tencent AI Lab, MoE Key Laboratory of High Confidence Software Technologies, Northeastern University)