Social recommendation method based on multi-feature heterogeneous graph neural networks
US11631147B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 22, 2022 |
| Grant date | Apr 18, 2023 |
| Priority date | — |
| Expiry date | Jun 22, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A social recommendation method based on a multi-feature heterogeneous graph neural network is provided and includes: extracting attribute information of users and topics to code; processing user coding information and topic coding information through a multi-layer perceptron to obtain initial feature vectors of the users and the topics; establishing a heterogeneous graph by taking the users and the topics as nodes; inputting the heterogeneous graph into a heterogeneous graph neural network to perform information transmission in combination with an attention mechanism, and updating the feature vectors; and performing similarity calculation on the feature vectors of the users, and selecting the user and the topic with the highest similarity with the feature vector of the user for recommendation. Social information can be mined more comprehensively, features of users and interested topics of the users can be deeply fused, and recommendation accuracy and user experience can be improved.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.