Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information.

Area of Science:

• Computer Science
• Machine Learning
• Data Mining

Background:

• Hashing is crucial for computing and storage efficiency.
• Deep learning enhances traditional hashing methods.
• Large-scale dynamic data presents challenges in memory consumption and model retraining.

Purpose of the Study:

• To propose a deep hash embedding method (FPHD) for converting entities with attribute information into embedded vectors.
• To address memory consumption issues caused by linear growth of tables in dynamic data scenarios.
• To overcome difficulties in retraining models with newly added entities.

Main Methods:

• Utilizes hashing for rapid entity feature extraction.
• Employs a deep neural network to learn implicit associations between entity features.
• Details encoding methods and algorithm flow using movie data as an example.

Main Results:

• Achieves rapid reuse of dynamic data models.
• Demonstrates significant improvements in time and space complexity compared to existing embedding algorithms.
• Effectively handles large-scale dynamic data addition challenges.

Conclusions:

• The proposed deep hash embedding algorithm (FPHD) offers a superior solution for efficient data representation and management.
• FPHD effectively reduces memory footprint and simplifies the integration of new data into existing models.
• This approach provides a scalable and efficient method for handling dynamic datasets in machine learning applications.