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Based on the analysis of the current situation of the development of an online medical community (Haodf.com) in China, this paper puts forward recommendation suggestions of finding the right hospital and doctor to promote the rapid integration of Internet technology and traditional medical services. A new recommendation model called Probabilistic Matrix Factorization integrated with Convolutional Neural Network (PMF-CNN) is proposed in the paper. Doctors\u2019 data in Haodf.com were used to evaluate the performance of our system. The model improves the performance of medical consultation recommendations by fusing review text and doctor information based on CNN (Convolutional Neural Network). Specifically, CNN is used to learn the feature representation of the review text and the doctors\u2019 information. Furthermore, the extended matrix factorization model is exploited to fuse the review information feature and the initial value of the doctors\u2019 information for recommendation. As is shown in the experimental results on Haodf.com datasets, the proposed PMF-CNN achieves better recommendation performances than the other state-of-the-art recommendation algorithms. And the recommendation system in an online medical website improves the utilization efficiency of doctors and the balance of public health resources allocation.<\/jats:p>","DOI":"10.1155\/2020\/8826557","type":"journal-article","created":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T02:20:05Z","timestamp":1602814805000},"page":"1-10","source":"Crossref","is-referenced-by-count":25,"title":["Online Doctor Recommendation with Convolutional Neural Network and Sparse Inputs"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1725-8206","authenticated-orcid":true,"given":"Yongjie","family":"Yan","sequence":"first","affiliation":[{"name":"School of Management, Harbin Institute of Technology, Harbin 150001, China"},{"name":"School of Mathematics and Computer Science, Jiangxi Science & Technology Normal University, Nanchang 330038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8794-8205","authenticated-orcid":true,"given":"Guang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Management, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangbin","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.25300\/misq\/2016\/40.1.11"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph16071273"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.4365"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.2196\/12126"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85820-3_1"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2005.99"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85820-3_3"},{"key":"8","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/978-3-540-72079-9_10","article-title":"Content-based recommendation systems","volume-title":"The Adaptive Web, Methods and Strategies of Web Personalization","author":"M. 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