55,748 research outputs found
Hierarchical Expert Recommendation on Community Question Answering Platforms
The community question answering (CQA) platforms, such as Stack Overflow, have become the primary source of answers to most questions in various topics. CQA platforms offer an opportunity for sharing and acquiring knowledge at a low cost, where users, many of whom are experts in a specific topic, can potentially provide high-quality solutions to a given question. Many recommendation methods have been proposed to match questions to potential good answerers. However, most existing methods have focused on modelling the user-question interaction — a user might answer multiple questions and a question might be answered by multiple users — using simple collaborative filtering approaches, overlooking the rich information in the question’s title and body when modelling the users’ expertise.
This project fills the research gap by thoroughly examining machine learning and deep learning approaches that can be applied to the expert recommendation problem. It proposes a Hierarchical Expert Recommendation (HER) model, a deep learning recommender system that recommends experts to answer a given question in the CQA platform. Although choosing a deep learning over a machine learning solution for this problem can be justified considering the degree of complexity of the available datasets, we assess performance of each family of methods and evaluate the trade-off between them to pick the perfect fit for our problem.
We analyzed various machine learning algorithms to determine their performances in the expert recommendation problem, which narrows down the potential ways for tackling this problem using traditional recommendation methods. Furthermore, we investigate the recommendation models based on matrix factorization to establish the baselines for our proposed model and shed light on the weaknesses and strengths of matrix- based solutions, which shape our final deep learning model. In the last section, we introduce the Hierarchical Expert Recommendation System (HER) that utilizes hierarchical attention-based neural networks to rep- resent the questions better and ultimately model the users’ expertise through user-question interactions. We conducted extensive experiments on a large real-world Stack Overflow dataset and benchmarked HER against the state-of-the-art baselines. The results from our extensive experiments show that HER outperforms the state-of-the-art baselines in recommending experts to answer questions in Stack Overflow
QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites
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.Comment: Accepted in the Proceedings of the 12th International AAAI Conference
on Web and Social Media (ICWSM 2018). June 2018. Stanford, CA, US
RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests
Various forms of Peer-Learning Environments are increasingly being used in
post-secondary education, often to help build repositories of student generated
learning objects. However, large classes can result in an extensive repository,
which can make it more challenging for students to search for suitable objects
that both reflect their interests and address their knowledge gaps. Recommender
Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution
to this problem by providing sophisticated filtering techniques to help
students to find the resources that they need in a timely manner. Here, a new
RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is
presented. The approach uses a collaborative filtering algorithm based upon
matrix factorization to create personalized recommendations for individual
students that address their interests and their current knowledge gaps. The
approach is validated using both synthetic and real data sets. The results are
promising, indicating RiPLE is able to provide sensible personalized
recommendations for both regular and cold-start users under reasonable
assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the
Journal of Educational Data Minin
Expert recommendation via tensor factorization with regularizing hierarchical topical relationships
© Springer Nature Switzerland AG 2018. Knowledge acquisition and exchange are generally crucial yet costly for both businesses and individuals, especially when the knowledge concerns various areas. Question Answering Communities offer an opportunity for sharing knowledge at a low cost, where communities users, many of whom are domain experts, can potentially provide high-quality solutions to a given problem. In this paper, we propose a framework for finding experts across multiple collaborative networks. We employ the recent techniques of tree-guided learning (via tensor decomposition), and matrix factorization to explore user expertise from past voted posts. Tensor decomposition enables to leverage the latent expertise of users, and the posts and related tags help identify the related areas. The final result is an expertise score for every user on every knowledge area. We experiment on Stack Exchange Networks, a set of question answering websites on different topics with a huge group of users and posts. Experiments show our proposed approach produces steady and premium outputs
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