cf419b1d-64a3-421c-956b-ceeb1faaeab120220517065646519naun:naunmdt@crossref.orgMDT DepositInternational Journal of Mathematical Models and Methods in Applied Sciences1998-014010.46300/9101http://www.naun.org/cms.action?id=2820110202211020221610.46300/9101.2022.16https://npublications.com/journals/ijmmas/2022.phpSiyuHuangSchool of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, ChinaXueyanHuangFujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou 362000, ChinaTaishengZengSchool of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, ChinaDanlinCaiSchool of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, ChinaDaxinZhuSchool of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, ChinaManufacturing industry requires a unique recommendation system to suggest products and raw materials, but its performance is often poor in massive data environment. In order to solve the similarity connection problem of large-scale real-time data, the optimized incremental similarity connection method which is used to deal with streaming data can concisely obtain the longest common additive sequence of two given input sequences. This paper, on the basis of the recursion equation, applies a very simple linear space algorithm to solve this problem and adopts new states to carry out similarity connection of incremental data. The experimental results demonstrate that this method can not only ensure the accuracy of real-time recommendation system but also greatly reduce the computed amount.5172022517202211211819https://npublications.com/journals/ijmmas/2022/a382001-019(2022).pdf10.46300/9101.2022.16.19https://npublications.com/journals/ijmmas/2022/a382001-019(2022).pdf10.1145/2414425.2414436Gedikli F, Jannach D . Improving Recommendation Accuracy Based on Item-Specific Tag Preferences. 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