A long command subsequence algorithm for manufacturing industry recommendation systems with similarity connection technology

Abstract

The manufacturing 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 optimised incremental similarity connection method which is used to deal with streaming data can be used to 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.

Keywords

• long command subsequence
• similarity connection
• recommendation system
• real-time
• manufacturing industry
• massive data
• memory computation
• 68-04