A Survey on Expert Recommendation in Community Question Answering

Abstract

Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify their questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.

Mining shape of expertise: A novel approach based on convolutional neural network

“…Over the past years, many research studies have conducted to solve this problem in different domains, each of which tried to improve the quality of ranking by dealing with a specific challenge [10,1]. In the past, expert finding methods were categorized into two graph-based [11,12,13,14] and topic modeling-based [15,16,17] groups.…”

“…Over the recent years, many research studies have been conducted to solve different challenging problems on CQA networks [1,2,5]. In CQA networks, documents are posted by registered users in the forms of questions and answers.…”

Expert Recommendation via Tensor Factorization with Regularizing Hierarchical Topical Relationships

“…Huang et al [10] take advantage of word embedding technology to rank experts both semantically and numerically. More relate works can be found in a survey by Wang et al [25].…”

Collaborative Recommendation of Temporally-Discounted Tag-Based Expertise for Community Question Answering

“…Expert recommendation [17] enables the timely sharing of high-quality knowledge for community question answering (CQA) [16]. Unfortunately, despite several previous research efforts, expert recommendation still remains problematic for various reasons.…”

“…Thirdly, supplementing content features with further auxiliary data (e.g., networks of user interactions) for more effective expert recommendation involves devising a plausible joint processing of such information. Hitherto, both sources of information have been combined mainly through simplistic schemes such as, e.g., linear interpolation [17].…”