Huang, X.; Chen, H.; Zhang, Z. Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information. Entropy2023, 25, 361.
Huang, X.; Chen, H.; Zhang, Z. Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information. Entropy 2023, 25, 361.
Huang, X.; Chen, H.; Zhang, Z. Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information. Entropy2023, 25, 361.
Huang, X.; Chen, H.; Zhang, Z. Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information. Entropy 2023, 25, 361.
Abstract
Most machine learning and deep learning algorithms can only use low-dimensional data as input, but the data that must be processed in practical applications is diverse and irregular. There are two main problems with big dynamic data. (1) The size of the embedding table grows linearly with the vocabulary size, resulting in massive memory consumption. (2) For different newly added vocabularies. To solve these two problems, this paper proposes a novel embedding algorithm that can learn attribute associations with entities based on deep and hash algorithms. Taking movie data as an example, the encoding method and the specific flow of the algorithm are presented in detail, and the effect of dynamic reuse of data models is realized. Compared with the four existing embedding algorithms which can fuse entity attribute information, the deep hash embedding algorithm proposed in this paper has obvious optimization of time and space complexity.
Keywords
hash embedding; deep learning; attribute information; entity coding
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
