89,525 research outputs found
Leveraging Social Foci for Information Seeking in Social Media
The rise of social media provides a great opportunity for people to reach out
to their social connections to satisfy their information needs. However,
generic social media platforms are not explicitly designed to assist
information seeking of users. In this paper, we propose a novel framework to
identify the social connections of a user able to satisfy his information
needs. The information need of a social media user is subjective and personal,
and we investigate the utility of his social context to identify people able to
satisfy it. We present questions users post on Twitter as instances of
information seeking activities in social media. We infer soft community
memberships of the asker and his social connections by integrating network and
content information. Drawing concepts from the social foci theory, we identify
answerers who share communities with the asker w.r.t. the question. Our
experiments demonstrate that the framework is effective in identifying
answerers to social media questions.Comment: AAAI 201
ExpFinder: An Ensemble Expert Finding Model Integrating -gram Vector Space Model and CO-HITS
Finding an expert plays a crucial role in driving successful collaborations
and speeding up high-quality research development and innovations. However, the
rapid growth of scientific publications and digital expertise data makes
identifying the right experts a challenging problem. Existing approaches for
finding experts given a topic can be categorised into information retrieval
techniques based on vector space models, document language models, and
graph-based models. In this paper, we propose , a new
ensemble model for expert finding, that integrates a novel -gram vector
space model, denoted as VSM, and a graph-based model, denoted as
\textit{\muCO-HITS}, that is a proposed variation of the CO-HITS algorithm.
The key of VSM is to exploit recent inverse document frequency weighting
method for -gram words and incorporates VSM into
\textit{\muCO-HITS} to achieve expert finding. We comprehensively evaluate
on four different datasets from the academic domains in
comparison with six different expert finding models. The evaluation results
show that is a highly effective model for expert finding,
substantially outperforming all the compared models in 19% to 160.2%.Comment: 15 pages, 18 figures, "for source code on Github, see
https://github.com/Yongbinkang/ExpFinder", "Submitted to IEEE Transactions on
Knowledge and Data Engineering
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
Review Paper on Answers Selection and Recommendation in Community Question Answers System
Nowadays, question answering system is more convenient for the users, users ask question online and then they will get the answer of that question, but as browsing is primary need for each an individual, the number of users ask question and system will provide answer but the computation time increased as well as waiting time increased and same type of questions are asked by different users, system need to give same answers repeatedly to different users. To avoid this we propose PLANE technique which may quantitatively rank answer candidates from the relevant question pool. If users ask any question, then system provide answers in ranking form, then system recommend highest rank answer to the user. We proposing expert recommendation system, an expert will provide answer of the question which is asked by the user and we also implement sentence level clustering technique in which a single question have multiple answers, system provide most suitable answer to the question which is asked by the user
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
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