Abstract Community Question Answering (CQA) websites can be claimed as the most major venues for knowledge sharing, and the most effective way of exchanging knowledge at present. Considering that massive amount of users are participating online and generating huge amount data, management of knowledge here systematically can be challenging. Expert recommendation is one of the major challenges, as it highlights users in CQA with potential expertise, which may help match unresolved questions with existing high quality answers while at the same time may help external services like human resource systems as another reference to evaluate their candidates. In this paper, we in this work we propose to exploring experts in CQA websites. We take advantage of recent distributed word representation technology to help summarize text chunks, and in a semantic view exploiting the relationships between natural language phrases to extract latent knowledge domains. By domains, the users’ expertise is determined on their historical performance, and a rank can be compute to given recommendation accordingly. In particular, Stack Overflow is chosen as our dataset to test and evaluate our work, where inclusive experiment shows our competence.
Apart from computing user expertise, question difficulty can also be a reference to infer experts. Both are interesting to Hanrahan and his group. In @cite_20 , their research propose to reveal question difficulty by mining question-answer events, and which is combined with the reputation score Stack Overflow provides. Alternatively, user events as giving up-votes and down-votes can also be utilized to determine user expertise( @cite_2 ). @cite_22 build user profiles to rank users. They reveal underlying connections between users and questions and appropriate users are recommended based on their interests. Models like Latent Dirichlet Allocation (LDA) and Segmented Topic Model (STM) for clustering are also inclusively compared during their experiments.
@cite_22 @cite_20 @cite_2
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Community Question Answering (CQA) websites provide a rapidly growing source of information in many areas. This rapid growth, while offering new opportunities, puts forward new challenges. In most CQA implementations there is little effort in directing new questions to the right group of experts. This means that experts are not provided with questions matching their expertise, and therefore new matching questions may be missed and not receive a proper answer. We focus on finding experts for a newly posted question. We investigate the suitability of two statistical topic models for solving this issue and compare these methods against more traditional Information Retrieval approaches. We show that for a dataset constructed from the Stackoverflow website, these topic models outperform other methods in retrieving a candidate set of best experts for a question. We also show that the Segmented Topic Model gives consistently better performance compared to the Latent Dirichlet Allocation Model. Supporting expert communities is becoming a 'must-have' capability whenever users are helping each other solve problems. Examples of these expert communities abound in the form of enthusiast communities, both inside and outside of organizations. In order to achieve success, these systems have to connect several different actors. In this paper we aim to inform the design of these Hybrid Intelligence Systems through the investigation of StackOverflow. Our focus in this paper is to develop indicators for hard problems and experts. The long-term goal of our study is to examine how complex problems are handled and dispatched across multiple experts. We outline implications for modeling these attributes and how they might inform better design in the future. Web-based communities have become important places for people to seek and share expertise. We find that networks in these communities typically differ in their topology from other online networks such as the World Wide Web. Systems targeted to augment web-based communities by automatically identifying users with expertise, for example, need to adapt to the underlying interaction dynamics. In this study, we analyze the Java Forum, a large online help-seeking community, using social network analysis methods. We test a set of network-based ranking algorithms, including PageRank and HITS, on this large size social network in order to identify users with high expertise. We then use simulations to identify a small number of simple simulation rules governing the question-answer dynamic in the network. These simple rules not only replicate the structural characteristics and algorithm performance on the empirically observed Java Forum, but also allow us to evaluate how other algorithms may perform in communities with different characteristics. We believe this approach will be fruitful for practical algorithm design and implementation for online expertise-sharing communities.