Goto

Collaborating Authors

Sun

AAAI Conferences

In this paper, we present a framework for Question Difficulty and Expertise Estimation (QDEE) in Community Question Answering sites (CQAs) such as Yahoo! Answers and Stack Overflow, which tackles a fundamental challenge in crowdsourcing: how to appropriately route and assign questions to users with the suitable expertise. This problem domain has been the subject of much research and includes both language-agnostic as well as language conscious solutions. We bring to bear a key language-agnostic insight: that users gain expertise and therefore tend to ask as well as answer more difficult questions over time. We use this insight within the popular competition (directed) graph model to estimate question difficulty and user expertise by identifying key hierarchical structure within said model. An important and novel contribution here is the application of social agony'' to this problem domain. Difficulty levels of newly posted questions (the cold-start problem) are estimated by using our QDEE framework and additional textual features. We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user.


Liang

AAAI Conferences

In this paper, we aim at tackling the problem of dynamic user profiling in the context of streams of short texts. Profiling users' expertise in such context is more challenging than in the case of long documents in static collection as it is difficult to track users' dynamic expertise in streaming sparse data. To obtain better profiling performance, we propose a streaming profiling algorithm (SPA). SPA first utilizes the proposed user expertise tracking topic model (UET) to track the changes of users' dynamic expertise and then utilizes the proposed streaming keyword diversification algorithm (SKDA) to produce top-k diversified keywords for profiling users' dynamic expertise at a specific point in time. Experimental results validate the effectiveness of the proposed algorithms.


The Data Science Venn Diagram -- Drew Conway

#artificialintelligence

On Monday we spent a lot of time talking about "where" a course on data science might exist at a university. The conversation was largely rhetorical, as everyone was well aware of the inherent interdisciplinary nature of the these skills; but then, why have I highlighted these three? First, none is discipline specific, but more importantly, each of these skills are on their own very valuable, but when combined with only one other are at best simply not data science, or at worst downright dangerous. For better or worse, data is a commodity traded electronically; therefore, in order to be in this market you need to speak hacker. This, however, does not require a background in computer science--in fact--many of the most impressive hackers I have met never took a single CS course.


QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites

arXiv.org Artificial Intelligence

In this paper, we present a framework for Question Difficulty and Expertise Estimation (QDEE) in Community Question Answering sites (CQAs) such as Yahoo! Answers and Stack Overflow, which tackles a fundamental challenge in crowdsourcing: how to appropriately route and assign questions to users with the suitable expertise. This problem domain has been the subject of much research and includes both language-agnostic as well as language conscious solutions. We bring to bear a key language-agnostic insight: that users gain expertise and therefore tend to ask as well as answer more difficult questions over time. We use this insight within the popular competition (directed) graph model to estimate question difficulty and user expertise by identifying key hierarchical structure within said model. An important and novel contribution here is the application of "social agony" to this problem domain. Difficulty levels of newly posted questions (the cold-start problem) are estimated by using our QDEE framework and additional textual features. We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user. Extensive experiments on real world CQAs such as Yahoo! Answers and Stack Overflow data demonstrate the improved efficacy of our approach over contemporary state-of-the-art models. The QDEE framework also allows us to characterize user expertise in novel ways by identifying interesting patterns and roles played by different users in such CQAs.


Agents for Expertise Location

AAAI Conferences

The problem of finding someone who might be able to help with a particular task or knowledge area exists everywhere, be it in groups of students or corporate settings. Time and effort are spent looking for relevant information when another person in the community could easily provide assistance. We have chosen to tackle this problem. Our approach to addressing the problem of finding people who can help is to use software agents to assist the search for expertise and mediate the information exchange process. Issues of availability, user profiling, privacy and incentives are involved.