{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:29:29Z","timestamp":1774355369802,"version":"3.50.1"},"reference-count":17,"publisher":"Wiley","license":[{"start":{"date-parts":[[2019,5,7]],"date-time":"2019-05-07T00:00:00Z","timestamp":1557187200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2019,5,7]]},"abstract":"<jats:p>The e-commerce recommendation system mainly includes content recommendation technology, collaborative filtering recommendation technology, and hybrid recommendation technology. The collaborative filtering recommendation technology is a successful application of personalized recommendation technology. However, due to the sparse data and cold start problems of the collaborative recommendation technology and the continuous expansion of data scale in e-commerce, the e-commerce recommendation system also faces many challenges. This paper has conducted useful exploration and research on the collaborative recommendation technology. Firstly, this paper proposed an improved collaborative filtering algorithm. Secondly, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network. Finally, we select a part of user communities from the user network projected by the user-item network as the candidate neighboring user set for the target user, thereby reducing calculation time and increasing recommendation speed and accuracy of the recommendation system. This paper has a perfect combination of social network technology and collaborative filtering technology, which can greatly increase recommendation system performance. This paper used the MovieLens dataset to test two performance indexes which include MAE and RMSE. The experimental results show that the improved collaborative filtering algorithm is superior to other two collaborative recommendation algorithms for MAE and RMSE performance.<\/jats:p>","DOI":"10.1155\/2019\/3560968","type":"journal-article","created":{"date-parts":[[2019,5,7]],"date-time":"2019-05-07T19:34:32Z","timestamp":1557257672000},"page":"1-11","source":"Crossref","is-referenced-by-count":35,"title":["An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9485-3084","authenticated-orcid":true,"given":"Xiaofeng","family":"Li","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Heilongjiang International University, Harbin 150025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-89743-1_56"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/jsyst.2016.2542179"},{"issue":"13","key":"4","first-page":"7087","volume":"10","year":"2014","journal-title":"BioTechnology: An Indian Journal"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-018-0517-4"},{"key":"7","year":"2007"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-6170-8_35"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-017-0842-9"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-018-0527-2"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-017-4849-9"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.72.026132"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1086\/jar.33.4.3629752"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.122653799"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.69.026113"},{"issue":"7","key":"18","volume":"2009","year":"2009","journal-title":"Journal of Statistical Mechanics: Theory and Experiment"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2006.07.023"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.80.016105"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1109\/gcis.2009.68"}],"container-title":["Mobile Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2019\/3560968.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2019\/3560968.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/misy\/2019\/3560968.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,7]],"date-time":"2019-05-07T19:34:33Z","timestamp":1557257673000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/misy\/2019\/3560968\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,7]]},"references-count":17,"alternative-id":["3560968","3560968"],"URL":"https:\/\/doi.org\/10.1155\/2019\/3560968","relation":{},"ISSN":["1574-017X","1875-905X"],"issn-type":[{"value":"1574-017X","type":"print"},{"value":"1875-905X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,7]]}}}