Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse

Conference Proceeding
Publication date: 
06/2018
Authors: 
Xingshan Zeng
Jing Li
Lu Wang
Nick Beauchamp
Sarah Shugars
Kam-Fai Wong
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 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.

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