{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:11:46Z","timestamp":1753884706599,"version":"3.41.2"},"reference-count":19,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","funder":[{"name":"Huaihai Outsourcing Service Vocational Education Group"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52074273"],"award-info":[{"award-number":["52074273"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Collaborative Innovation Center for Building Energy Saving and Construct Technology Project","award":["SJXTBZ2110","SJXTBZ2109","SJXTBS2125","SJXTZD2103"],"award-info":[{"award-number":["SJXTBZ2110","SJXTBZ2109","SJXTBS2125","SJXTZD2103"]}]},{"name":"Jiangsu Qinglan Project"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:p> Personalized recommendation systems learn user preference characteristics by analyzing behavioral data such as ratings and comments generated by users in the Internet, and provide precise recommendations for individual users accordingly. However, in real life, users often conduct group activities like group buying and traveling together. How to recommend for groups has become a heated research topic in recent years. <\/jats:p><jats:p> Most existing group recommendation algorithms are recommended for given divided groups by collectively combining the preferences of members in the group. However, in most cases, users\u2019 group properties are fickle. As the results of group detection are decisive to the performance of group recommendation, group detection is particularly important to the group recommendation algorithm. After analyzing problems of existing group recommendation algorithms, this paper proposes the density peak clustering group detection algorithm based on GRU-CNN and the group recommendation algorithm based on the mechanism. <\/jats:p><jats:p> With respect to group detection, most of the existing group detection algorithms suffer from certain deficiencies: First, depending solely on the users\u2019 static preference features while ignoring the variation of users\u2019 interest over time when finding the group structure in the network; second, group division based on users\u2019 topic features extracted from reviews is difficult to support further digging of the in-depth features in reviews. To address the above-mentioned problems, this paper proposes a density peak clustering group detection algorithm based on CNN-GRU. It would first extract representative keywords in the reviews with LDA topic model, and then model time series information based on GRU attaining users\u2019 dynamic topic features. Coupling with deeper characteristics cored out by CNN, density peak clustering algorithm completes its group detection finally. Experiments on real dataset indicate that the features mined by the fusion depth neural network model effectively capture users\u2019 dynamic preferences, and yield better results of group detection than that of existing algorithms. <\/jats:p>","DOI":"10.1142\/s0218001423590115","type":"journal-article","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T06:47:02Z","timestamp":1679035622000},"source":"Crossref","is-referenced-by-count":0,"title":["A GRU-CNN Algorithm Leveraging on User Reviews"],"prefix":"10.1142","volume":"37","author":[{"given":"Chao","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, P. R. China"},{"name":"School of Information and Electronics Engineering, Jiangsu Vocational Institute of Architectural Technology, Xuzhou 221000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongsheng","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, P. R. China"},{"name":"The 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoli","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, P. R. China"},{"name":"Xuzhou Intelligent Machine Vision Engineering and Technology Center, Xuzhou 221000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yandong","family":"Liu","sequence":"additional","affiliation":[{"name":"Xuzhou City Hospital of TCM, Xuzhou 221000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2001-8139","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Electronics Engineering, Jiangsu Vocational Institute of Architectural Technology, Xuzhou 221000, P. R. China"},{"name":"Xuzhou Intelligent Machine Vision Engineering and Technology Center, Xuzhou 221000, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2023,4,28]]},"reference":[{"key":"S0218001423590115BIB001","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.neucom.2016.11.019","volume":"226","author":"Bai X.","year":"2017","journal-title":"Neurocomputing"},{"key":"S0218001423590115BIB002","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.ins.2011.11.037","volume":"189","author":"Garcia I.","year":"2012","journal-title":"Inf. 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