Mathematical Expression Recommendation Model Based on Genetic Algorithm

In scientific research, education, engineering and many other fields, mathematical expressions as a kind of complex, semantic strong important information. People need to recognize the two-dimensional structure of expressions and the semantic content of expressions in its retrieval. However, because the mathematical expression contains many symbols such as numbers, operators and letters, and the structure is complex and diverse, it is difficult to realize the index and retrieval of the full text. Based on this, this paper adopts genetic algorithm to improve and design the mathematical expression recommendation model. Firstly, the location selection mode of mathematical expression expression is added to the input end of mathematical expression query, so that the search results are initially close to user preferences. Then, the genetic algorithm is added to the process of similarity calculation, and a collaborative filtering recommendation algorithm based on genetic algorithm to improve the similarity is obtained. At the same time, similar user characteristics based on genetic algorithm are introduced to improve the accuracy of recommendation. Experimental results show that in K-nearest neighbor IBCF, with the increase of k value, the effect of GA recommendation presents a gradual optimization process. Moreover, in terms of the recall rate and accuracy recommended by the number of 1500 mathematical expressions, the recall rate and accuracy of GA improved similarity arithmetic are 89% and 97%. High recall rate and accuracy were obtained.