Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse

Xingshan Zeng, Jing Li, Lu Wang, Nicholas Beauchamp, Sarah Shugars, Kam-Fai Wong


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 methodfor microblog conversation recommendation.While much prior work has focused on postlevel recommendation, we exploit both theconversational context, and user content andbehavior preferences. We propose a statisticalmodel that jointly captures: (1) topics for representing user interests and conversation content, and (2) discourse modes for describinguser replying behavior and conversation dynamics. Experimental results on two Twitterdatasets demonstrate that our system outperforms methods that only model content without considering discourse.

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