Name
Abstract | ||
|---|---|---|
Mathematics is fundamental to natural science. Its content is extensive and profound, and the matrix is an important part of mathematics. It can be seen in many fields. For example, in game theory and economics, the payoff matrix is used to represent the payoff of two game objects in various decision modes. Early cryptographic techniques, such as the Hill code, also used matrix. In the field of chemistry, where quantum theory is used to discuss molecular bonds and spectra, the matrix is applied. In physics, matrices have applications in electronics, mechanics, optics and quantum physics. In the field of computer engineering, it is more widely used, such as computer image processing, 3D animation production, text mining and its' inverted index technology. Therefore, this paper mainly focuses on matrix operations and its application in personalized recommendation methods. This paper adopts recommendation method based on bipartite graph. This method improves the accuracy of recommendation effectively. The experimental results verify its feasibility. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICIS.2018.8466378 | 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) |
Keywords | Field | DocType |
Matrix operation,Personalized recommendation,Collaborative filtering,Bipartite graph | Inverted index,Matrix (mathematics),Image processing,Game theory,Normal-form game,Computer animation,Computer engineering,Matrix multiplication,Stochastic game | Conference |
ISBN | Citations | PageRank |
978-1-5386-5893-2 | 0 | 0.34 |
References | Authors | |
2 | 2 | |