{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T15:21:42Z","timestamp":1769354502370,"version":"3.49.0"},"reference-count":56,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T00:00:00Z","timestamp":1591315200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2020,6,5]]},"abstract":"Purpose<\/jats:title>In community question answering (CQA), people who answer questions assume readers have mastered the content in the answers. Nevertheless, some readers cannot understand all content. Thus, there is a need for further explanation of the concepts that appear in the answers. Moreover, the large number of question and answer (Q&A) documents make manual retrieval difficult. This paper aims to alleviate these issues for CQA websites.<\/jats:p><\/jats:sec>Design\/methodology\/approach<\/jats:title>In the paper, an algorithm for recommending explanatory Q&A documents is proposed. Q&A documents are modeled with the biterm topic model (BTM) (Yan et al.<\/jats:italic>, 2013). Then, the growing neural gas (GNG) algorithm (Fritzke, 1995) is used to cluster Q&A documents. To train multiple classifiers, three features are extracted from the Q&A categories. Thereafter, an ensemble classification model is constructed to identify the explanatory relationships. Finally, the explanatory Q&A documents are recommended.<\/jats:p><\/jats:sec>Findings<\/jats:title>The GNG algorithm shows good clustering performance. The ensemble classification model performs better than other classifiers. The both effect and quality scores of explanatory Q&A recommendations are high. These scores indicate the practicality and good performance of the proposed recommendation algorithm.<\/jats:p><\/jats:sec>Research limitations\/implications<\/jats:title>The proposed algorithm alleviates information overload in CQA from the new perspective of recommending explanatory knowledge. It provides new insight into research on recommendations in CQA. Moreover, in practice, CQA websites can use it to help retrieve Q&A documents and facilitate understanding of their contents. However, the algorithm is for the general recommendation of Q&A documents which does not consider individual personalized characteristics. In future work, personalized recommendations will be evaluated.<\/jats:p><\/jats:sec>Originality\/value<\/jats:title>A novel explanatory Q&A recommendation algorithm is proposed for CQA to alleviate the burden of manual retrieval and Q&A overload. The novel GNG clustering algorithm and ensemble classification model provide a more accurate way to identify explanatory Q&A documents. The method of ranking the explanatory Q&A documents improves the effectiveness and quality of the recommendation. The proposed algorithm improves the accuracy and efficiency of retrieving explanatory Q&A documents. It assists users in grasping answers easily.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-11-2019-0201","type":"journal-article","created":{"date-parts":[[2020,6,8]],"date-time":"2020-06-08T11:51:37Z","timestamp":1591617097000},"page":"437-459","source":"Crossref","is-referenced-by-count":4,"title":["Explanatory Q&A recommendation algorithm in community question answering"],"prefix":"10.1108","volume":"54","author":[{"given":"Ming","family":"Li","sequence":"first","affiliation":[]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[]},{"given":"YingCheng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020082512233777900_ref001","first-page":"1","article-title":"The Kendall rank correlation coefficient","year":"1955","journal-title":"Encyclopedia of Measurement and Statistics"},{"key":"key2020082512233777900_ref002","first-page":"1","article-title":"Item-based collaborative filtering recommendation algorithms","year":"2012"},{"issue":"3","key":"key2020082512233777900_ref003","doi-asserted-by":"crossref","first-page":"74","DOI":"10.2307\/2986801","article-title":"Rank correlation methods","volume":"20","year":"1971","journal-title":"The Statistician"},{"key":"key2020082512233777900_ref004","first-page":"331","article-title":"Hybrid recommender systems: survey and experiments","volume-title":"User Modeling and User-Adapted Interaction","year":"2002"},{"issue":"1","key":"key2020082512233777900_ref005","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1146\/annurev.neuro.31.060407.125639","article-title":"Spike timing\u2013dependent plasticity: a Hebbian learning rule","volume":"31","year":"2008","journal-title":"Annual Review of Neuroscience, Annual Reviews"},{"issue":"1-4","key":"key2020082512233777900_ref006","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/S0925-2312(00)00305-2","article-title":"Efficient training and improved performance of multilayer perceptron in pattern classification","volume":"34","year":"2000","journal-title":"Neurocomputing"},{"key":"key2020082512233777900_ref007","first-page":"809","article-title":"Clustering microarray data within amorphous computing paradigm and growing neural gas algorithm","year":"2006"},{"key":"key2020082512233777900_ref008","first-page":"117","article-title":"Question retrieval for community-based question answering via heterogeneous social influential network","volume-title":"Neurocomputing","year":"2018"},{"key":"key2020082512233777900_ref009","first-page":"250","article-title":"An improved method to construct basic probability assignment based on the confusion matrix for classification problem","volume-title":"Information Sciences","year":"2016"},{"key":"key2020082512233777900_ref010","first-page":"21","article-title":"A hybrid model to predict best answers in question answering communities","volume-title":"Egyptian Informatics Journal","year":"2018"},{"key":"key2020082512233777900_ref011","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.eswa.2015.12.016","article-title":"Search clicks analysis for discovering temporally anchored questions in community Question Answering","volume":"50","year":"2016","journal-title":"Expert Systems with Applications"},{"key":"key2020082512233777900_ref012","first-page":"112","article-title":"Integrating heterogeneous sources for predicting question temporal anchors across Yahoo! Answers","volume-title":"Information Fusion","year":"2019"},{"issue":"5","key":"key2020082512233777900_ref013","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203450","article-title":"Greedy function approximation: a gradient boosting machine","volume":"29","year":"2001","journal-title":"Annals of Statistics"},{"issue":"9","key":"key2020082512233777900_ref014","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1016\/0893-6080(94)90091-4","article-title":"Growing cell structures-A self-organizing network for unsupervised and supervised learning","volume":"7","year":"1994","journal-title":"Neural Networks"},{"key":"key2020082512233777900_ref015","first-page":"625","article-title":"A growing neural gas network learns topologies","volume":"7","year":"1995","journal-title":"Advances in Neural Information Processing Systems"},{"key":"key2020082512233777900_ref016","first-page":"1","article-title":"User correlation model for question recommendation in community question answering","volume-title":"Applied Intelligence","year":"2019"},{"key":"key2020082512233777900_ref017","first-page":"1524","article-title":"The hot keyphrase extraction based on TF*PDF","year":"2011"},{"key":"key2020082512233777900_ref018","first-page":"21","article-title":"Case-based reasoning and agent based job offer recommender system","volume-title":"Advances in Intelligent Systems and Computing","year":"2019"},{"key":"key2020082512233777900_ref019","first-page":"68","article-title":"Exploiting content quality and question difficulty in CQA reputation systems","volume-title":"Lecture Notes in Computer Science","year":"2016"},{"key":"key2020082512233777900_ref020","first-page":"261","article-title":"Recommendation systems: principles, methods and evaluation","volume-title":"Egyptian Informatics Journal","year":"2015"},{"key":"key2020082512233777900_ref021","first-page":"93","article-title":"A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups","volume-title":"Information Sciences","year":"2013"},{"key":"key2020082512233777900_ref022","first-page":"463","article-title":"Formulation of a hybrid expertise retrieval system in community question answering services","volume-title":"Applied Intelligence","year":"2019"},{"issue":"5","key":"key2020082512233777900_ref023","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1109\/TMM.2014.2311012","article-title":"Using dynamically promoted experts for music recommendation","volume":"16","year":"2014","journal-title":"IEEE Transactions on Multimedia"},{"key":"key2020082512233777900_ref024","first-page":"113088","article-title":"A hybrid recommendation system for Q&A documents","volume-title":"Expert Systems with Applications","year":"2020"},{"key":"key2020082512233777900_ref025","first-page":"112923","article-title":"Knowledge map construction for question and answer archives","volume-title":"Expert Systems with Applications","year":"2020"},{"issue":"11","key":"key2020082512233777900_ref026","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1109\/TKDE.2010.193","article-title":"On the design and analysis of the privacy-preserving SVM classifier","volume":"23","year":"2011","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"key2020082512233777900_ref027","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2013.08.034","article-title":"Personalized news recommendation via implicit social experts","volume":"254","year":"2014","journal-title":"Information Sciences"},{"key":"key2020082512233777900_ref028","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.knosys.2013.12.017","article-title":"QA document recommendations for communities of question-answering websites","volume":"57","year":"2014","journal-title":"Knowledge-Based Systems"},{"issue":"1","key":"key2020082512233777900_ref029","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1002\/asi.23155","article-title":"Complementary QA network analysis for QA retrieval in social question-answering websites","volume":"66","year":"2015","journal-title":"Journal of the Association for Information Science and Technology"},{"key":"key2020082512233777900_ref030","first-page":"397","article-title":"A \u2018neural-gas\u2019 network learns topologies","year":"1991","journal-title":"Artificial Neural Networks"},{"key":"key2020082512233777900_ref031","first-page":"47","article-title":"Book Recommendation System through content based and collaborative filtering method","year":"2016"},{"key":"key2020082512233777900_ref032","first-page":"200","volume-title":"Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, June 5, 2018","year":"2000"},{"key":"key2020082512233777900_ref033","first-page":"257","article-title":"Question temporality: identification and uses","year":"2012"},{"issue":"1","key":"key2020082512233777900_ref034","first-page":"37","article-title":"Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation","volume":"2","year":"2011","journal-title":"Informedness, Markedness and Correlation"},{"key":"key2020082512233777900_ref035","first-page":"334","volume-title":"Comparative Analysis of Decision Tree Classification Algorithms, International Journal of Current Engineering and Technology","year":"2013"},{"key":"key2020082512233777900_ref036","first-page":"164","article-title":"A recommender system of reviewers and experts in reviewing problems","volume-title":"Knowledge-Based Systems","year":"2016"},{"key":"key2020082512233777900_ref037","first-page":"791","article-title":"Finding expert users in community question answering","year":"2012","journal-title":"Dl.Acm.Org"},{"key":"key2020082512233777900_ref038","first-page":"53","article-title":"Finding and ranking high-quality answers in community question answering sites","volume-title":"Global Journal of Flexible Systems Management","year":"2018"},{"issue":"2","key":"key2020082512233777900_ref039","doi-asserted-by":"crossref","first-page":"88","DOI":"10.18201\/ijisae.2019252786","article-title":"Performance analysis of ANN and naive Bayes classification algorithm for data classification","volume":"7","year":"2019","journal-title":"International Journal of Intelligent Systems and Applications in Engineering"},{"key":"key2020082512233777900_ref040","first-page":"163","article-title":"A content-based recommendation algorithm for learning resources","volume-title":"Multimedia Systems","year":"2018"},{"key":"key2020082512233777900_ref041","article-title":"QDEE: question difficulty and expertise estimation in community question answering sites","year":"2018"},{"key":"key2020082512233777900_ref042","first-page":"3143","article-title":"FSMJ: feature selection with maximum Jensen-Shannon divergence for text categorization","year":"2016"},{"key":"key2020082512233777900_ref043","first-page":"667","article-title":"An improved collaborative movie recommendation system using computational intelligence","volume-title":"Journal of Visual Languages and Computing","year":"2014"},{"key":"key2020082512233777900_ref044","first-page":"901","article-title":"Personalized recommendation for new questions in community question answering","year":"2016"},{"key":"key2020082512233777900_ref045","first-page":"17","article-title":"Answer selection and expert finding in community question answering services","volume-title":"Program","year":"2017"},{"key":"key2020082512233777900_ref046","volume-title":"The Whole is Greater than the Sum of its Parts: Towards the Effectiveness of Voting Ensemble Classifiers for Complex Word Identification","year":"2018"},{"key":"key2020082512233777900_ref047","first-page":"29","article-title":"Collaborative filtering and deep learning based recommendation system for cold start items","volume-title":"Expert Systems with Applications","year":"2017"},{"key":"key2020082512233777900_ref048","first-page":"1445","article-title":"A biterm topic model for short texts","year":"2013"},{"issue":"8","key":"key2020082512233777900_ref049","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/TPAMI.2016.2605085","article-title":"Social collaborative filtering by trust","volume":"39","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"key2020082512233777900_ref050","first-page":"37","article-title":"Combining content-based and collaborative filtering for job recommendation system: a cost-sensitive Statistical Relational Learning approach","volume-title":"Knowledge-Based Systems","year":"2017"},{"key":"key2020082512233777900_ref051","first-page":"478","article-title":"User-based collaborative-filtering recommendation algorithms on Hadoop","year":"2010"},{"key":"key2020082512233777900_ref052","first-page":"72","article-title":"Understanding the formation mechanism of high-quality knowledge in social question and answer communities: a knowledge co-creation perspective","volume-title":"International Journal of Information Management","year":"2019"},{"issue":"4","key":"key2020082512233777900_ref053","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1109\/TKDE.2014.2356461","article-title":"Expert finding for question answering via graph regularized matrix completion","volume":"27","year":"2015","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"key2020082512233777900_ref054","first-page":"05350","article-title":"A deep learning approach for expert identification in question answering communities","volume":"1711","year":"2017"},{"key":"key2020082512233777900_ref055","first-page":"2239","article-title":"Improving question retrieval in community question answering using world knowledge","year":"2013","journal-title":"Twenty-Third International Joint Conference on Artificial Intelligence"},{"issue":"January","key":"key2020082512233777900_ref056","first-page":"3000","article-title":"Expert finding for community-based question answering via ranking metric network learning","volume":"2016","year":"2016","journal-title":"IJCAI International Joint Conference on Artificial Intelligence"}],"container-title":["Data Technologies and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-11-2019-0201\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-11-2019-0201\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T23:15:27Z","timestamp":1753398927000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/dta\/article\/54\/4\/437-459\/20956"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,5]]},"references-count":56,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,6,5]]}},"alternative-id":["10.1108\/DTA-11-2019-0201"],"URL":"https:\/\/doi.org\/10.1108\/dta-11-2019-0201","relation":{},"ISSN":["2514-9288"],"issn-type":[{"value":"2514-9288","type":"print"}],"subject":[],"published":{"date-parts":[[2020,6,5]]}}}