@inproceedings{zeng-etal-2018-microblog,
title = "Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse",
author = "Zeng, Xingshan and
Li, Jing and
Wang, Lu and
Beauchamp, Nicholas and
Shugars, Sarah and
Wong, Kam-Fai",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1035",
doi = "10.18653/v1/N18-1035",
pages = "375--385",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse
%A Zeng, Xingshan
%A Li, Jing
%A Wang, Lu
%A Beauchamp, Nicholas
%A Shugars, Sarah
%A Wong, Kam-Fai
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F zeng-etal-2018-microblog
%X 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.
%R 10.18653/v1/N18-1035
%U https://aclanthology.org/N18-1035
%U https://doi.org/10.18653/v1/N18-1035
%P 375-385
Markdown (Informal)
[Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse](https://aclanthology.org/N18-1035) (Zeng et al., NAACL 2018)
ACL
- Xingshan Zeng, Jing Li, Lu Wang, Nicholas Beauchamp, Sarah Shugars, and Kam-Fai Wong. 2018. Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 375–385, New Orleans, Louisiana. Association for Computational Linguistics.