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Tracking user-role evolution via topic modeling in community question answering

机译：通过社区问答中的主题建模跟踪用户角色的演变

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摘要

Community question answering (CQA) services that enable users to ask and answer questions are popular on the internet. Each user can simultaneously play the roles of asker and answerer. Some work has aimed to model the roles of users for potential applications in CQA. However, the dynamic characteristics of user roles have not been addressed. User roles vary over time. This paper explores user representation by tracking user-role evolution, which could enable several potential applications in CQA, such as question recommendation. We believe this paper is the first to track user-role evolution and investigate its influence on the performance of question recommendation in CQA. Moreover, we propose a time-aware role model (TRM) to effectively track user-role evolution. With different independence assumptions, two variants of TRM are developed. Finally, we present the TRM-based approach to question recommendation, which provides a mechanism to naturally integrate the user-role evolution with content relevance between the answerer and the question into a unified probabilistic framework. Experiments using real-world data from Stack Overflow show that (1) the TRM is valid for tracking user-role evolution, and (2) compared with baselines utilizing role based methods, our TRM-based approach consistently and significantly improves the performance of question recommendation. Hence, our approach could enable several potential applications in CQA.
机译：使用户能够提出和回答问题的社区问题解答（CQA）服务在Internet上很流行。每个用户可以同时扮演询问者和应答者的角色。一些工作旨在为CQA中潜在应用程序的用户角色建模。但是，用户角色的动态特征尚未解决。用户角色随时间变化。本文通过跟踪用户角色的演变来探索用户表示，这可能会在CQA中启用一些潜在的应用程序，例如问题推荐。我们认为本文是第一个跟踪用户角色演变并研究其对CQA中问题推荐性能的影响的文章。此外，我们提出了一种时间感知角色模型（TRM），以有效跟踪用户角色的演变。在不同的独立性假设下，开发了TRM的两个变体。最后，我们提出了基于TRM的问题推荐方法，该方法提供了一种机制，可以将用户角色演变与应答者和问题之间的内容相关性自然地集成到一个统一的概率框架中。使用来自Stack Overflow的真实数据的实验表明（1）TRM有效跟踪用户角色演变，并且（2）与使用基于角色的方法的基线相比，我们基于TRM的方法始终如一并显着提高了问题的性能建议。因此，我们的方法可以在CQA中启用多个潜在的应用程序。

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