Dynamic Online Conversation Recommendation
37836Authors
nan
Abstract
AbstractTrending topics in social media content evolve over time, and it is therefore crucial to understand social media users and their interpersonal communications in a dynamic manner. Here we study dynamic online conversation recommendation, to help users engage in conversations that satisfy their evolving interests. While most prior work assumes static user interests, our model is able to capture the temporal aspects of user interests, and further handle future conversations that are unseen during training time. Concretely, we propose a neural architecture to exploit changes of user interactions and interests over time, to predict which discussions they are likely to enter. We conduct experiments on large-scale collections of Reddit conversations, and results on three subreddits show that our model significantly outperforms state-of-the-art models that make a static assumption of user interests. We further evaluate on handling “cold start”, and observe consistently better performance by our model when considering various degrees of sparsity of user’s chatting history and conversation contexts. Lastly, analyses on our model outputs indicate user interest change, explaining the advantage and efficacy of our approach.
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Full Content
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3331–3341 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 3331 Dynamic Online Conversation Recommendation Xingshan Zeng1,2, Jing Li3, Lu Wang4, Zhiming Mao1,2, Kam-Fai Wong1,2 1The Chinese University of Hong Kong, Hong Kong, China 2MoE Key Laboratory of High Confidence Software Technologies, China 3Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China 4Khoury College of Computer Sciences, Northeastern University, Boston, United States 1,2{xszeng,zmmao,kfwong}@se.cuhk.edu.hk 3jing-amelia.li@polyu.edu.hk, 4luwang@ccs.neu.edu Abstract Trending topics in social media content evolve over time, and it is therefore crucial to un- derstand social media users and their interper- sonal communications in a dynamic manner. In this research we study dynamic online con- versation recommendation, to help users en- gage in conversations that satisfy their evolv- ing interests. Different from works in conver- sation recommendation which assume static user interests, our model captures the tempo- ral aspects of user interests. Moreover, our model can cater for cold start problem where conversations are new and unseen in training. We propose a neural architecture to analyze changes of user interactions and interests over time, whose result is used to predict which dis- cussions the users are likely to enter. We con- duct experiments on large-scale collections of Reddit conversations. Results on three subred- dits show that our model significantly outper- forms state-of-the-art models based on static assumption of user interests. We further eval- uate performance in cold start, and observe consistently better performance by our model when considering various degrees of sparsity of user’s chatting history and conversation con- texts. Lastly, our analysis also confirms the change of user interests. This further justify the advantage and efficacy of our model. 1 Introduction Online social media platforms are popular outlets for individuals to exchange viewpoints and discuss topics they are interested in. However, the huge volume of online conversations produced daily hin- ders people’s capability of finding the information they are interested in. As a result, there is pressing demand for developing a conversation recommen- dation engine that tracks ongoing conversations and recommends suitable ones to users. Viewing the deluge of information streaming through social media, it is not hard to envision that [T1] In the UK they can request your encryption keys… …… [T2] … I doubt we are seeing the banning of encryption… in the ease of the authorities to go rummaging about your privacy. [T1] …where each country or group of countries gets to play with its own Internet, either making them secure or making them for surveillance. …… [T2] …but then again it kind of defeats the purpose of the Internet to go and fracture it like that… [T1] It's a bit like the Ubuntu variants that exist. In theory, one merely has to install the desired DE and select it at log in, but we still have those official DE variants to pick from. [T1] ksplice has existed for some time, but became part of the Oracle family. …… [T2] I've no …
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Labeled Text
AbstractTrending topics in social media content evolve over time, and it is therefore crucial to understand social media users and their interpersonal communications in a dynamic manner. Here we study dynamic online conversation recommendation, to help users engage in conversations that satisfy their evolving interests. While most prior work assumes static user interests, our model is able to capture the temporal aspects of user interests, and further handle future conversations that are unseen during training time. Concretely, we propose a neural architecture to exploit changes of user interactions and interests over time, to predict which discussions they are likely to enter. We conduct experiments on large-scale collections of Reddit conversations, and results on three subreddits show that our model significantly outperforms state-of-the-art models that make a static assumption of user interests. We further evaluate on handling “cold start”, and observe consistently better performance by our model when considering various degrees of sparsity of user’s chatting history and conversation contexts. Lastly, analyses on our model outputs indicate user interest change, explaining the advantage and efficacy of our approach.
Publication Details
- Paper ID:
- 37836
- Year:
- 2020
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan