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However, it is usually difficult for community members to efficiently find appropriate peers for social support exchange due to the tremendous volume of users and their generated content. Most of the existing user recommendation systems fail to effectively utilize the rich social information in social media, which can lead to unsatisfactory recommendation performance. The purpose of this study is to propose a novel user recommendation method for OHCs to fill this research gap.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This study proposed a user recommendation method that utilized the adapted matrix factorization (MF) model. The implicit user behavior networks and the user influence relationship (UIR) network were constructed using the various social information found in OHCs, including user-generated content (UGC), user profiles and user interaction records. An experiment was conducted to evaluate the effectiveness of the proposed approach based on a dataset collected from a famous online health community.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The experimental results demonstrated that the proposed method outperformed all baseline models in user recommendation using the collected dataset. The incorporation of social information from OHCs can significantly improve the performance of the proposed recommender system.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>This study can help users build valuable social connections efficiently, enhance communication among community members, and potentially contribute to the sustainable prosperity of OHCs.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This study introduces the construction of the UIR network in OHCs by integrating various social information. The conventional MF model is adapted by integrating the constructed UIR network for user recommendation.<\/jats:p><\/jats:sec>","DOI":"10.1108\/intr-09-2020-0501","type":"journal-article","created":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T00:57:51Z","timestamp":1629766671000},"page":"2190-2218","source":"Crossref","is-referenced-by-count":14,"title":["User recommendation in online health communities using adapted matrix factorization"],"prefix":"10.1108","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9569-5938","authenticated-orcid":false,"given":"Hangzhou","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1762-4744","authenticated-orcid":false,"given":"Huiying","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2021,8,25]]},"reference":[{"key":"key2021122215521626000_ref001","doi-asserted-by":"crossref","first-page":"15608","DOI":"10.1109\/ACCESS.2018.2810062","article-title":"Social media recommender systems: review and open research issues","volume":"6","year":"2018","journal-title":"IEEE Access"},{"issue":"8","key":"key2021122215521626000_ref002","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1002\/spe.2828","article-title":"A semantic and social-based collaborative recommendation of friends in social networks","volume":"50","year":"2020","journal-title":"Software-Practice and Experience"},{"issue":"2","key":"key2021122215521626000_ref003","doi-asserted-by":"crossref","first-page":"e12634","DOI":"10.1111\/exsy.12634","article-title":"Recommendation of users in social networks: a semantic and social based classification approach","volume":"38","year":"2021","journal-title":"Expert Systems"},{"issue":"6","key":"key2021122215521626000_ref004","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1016\/S0277-9536(00)00065-4","article-title":"From social integration to health: Durkheim in the new millennium","volume":"51","year":"2000","journal-title":"Social Science and Medicine"},{"key":"key2021122215521626000_ref005","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.compeleceng.2019.05.002","article-title":"Recommending similar users using moving patterns in mobile social networks","volume":"77","year":"2019","journal-title":"Computers and Electrical Engineering"},{"issue":"1","key":"key2021122215521626000_ref006","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1037\/0033-2909.119.1.111","article-title":"Culture and conformity: a meta-analysis of studies using Asch's (1952b, 1956) line judgment task","volume":"119","year":"1996","journal-title":"Psychological Bulletin"},{"key":"key2021122215521626000_ref007","first-page":"204","article-title":"Relationship recommender system in a business and employment-oriented social network","volume":"433","year":"2018","journal-title":"Information Sciences"},{"key":"key2021122215521626000_ref008","first-page":"785","article-title":"Xgboost: a scalable tree boosting system","year":"2016"},{"key":"key2021122215521626000_ref009","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.knosys.2018.05.040","article-title":"Matrix factorization for recommendation with explicit and implicit feedback","volume":"158","year":"2018","journal-title":"Knowledge-Based Systems"},{"issue":"8","key":"key2021122215521626000_ref010","doi-asserted-by":"crossref","first-page":"2889","DOI":"10.1016\/j.eswa.2012.12.006","article-title":"A unified framework for recommending items, groups and friends in social media environment via mutual resource fusion","volume":"40","year":"2013","journal-title":"Expert Systems with Applications"},{"key":"key2021122215521626000_ref011","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.dss.2016.06.017","article-title":"Who should you follow? 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