Knowledge graph-based approach for intelligent recommendation of communication solutions

Abstract

In order to effectively solve the problems of inefficiency and single form in the generation process of communication solutions, a knowledge graph-based intelligent recommendation method for communication solutions is proposed. By constructing a knowledge map of solutions and mapping the entities and relationships in the knowledge map to a lowdimensional and dense vector space based on the TransH model, the solution similarity is calculated; the user-rating matrix is generated based on the collaborative filtering recommendation algorithm, and the solution similarity is calculated based on the matrix; the solution similarity based on the knowledge map and the user behaviour is fused to calculate the comprehensive similarity, and this is used to generate a recommendation list to overcome the problem of inefficient and unidimensional communication solutions. The recommendation list is generated based on the combined similarity, completing the intelligent recommendation process for communication solutions with significantly higher efficiency compared to the manual calculations.