CGRS: Collaborative Knowledge Propagation Graph Attention Network for Recipes Recommendation

Abstract

In the age of big data, recipe recommendation is of great significance. It can recommend recipes in line with the user's eating habits in massive data. Compared with other recommendation tasks, recipe recommendation is influenced by multiple aspects and requires fine-grained learning to obtain entity representations. Therefore, the traditional recommendation method cannot meet people's requirements. In this paper, we propose the Collaborative Knowledge Propagation Graph Attention Network for Recipes Recommendation (CGRS). This method designs collaborative information propagation to make full use of user interaction and recipe attribute information to meet the needs of multiple influencing factors. Use the graph attention feature learning network to obtain the high-order feature information of the entity to meet the demand for fine-grained representation. Specifically, the method first obtains the multi-hop triplet sets of users and recipes through a collaborative message propagation strategy. Then utilises a graph attention feature learning layer to learn the topological proximity structure features of the triplet sets. Obtain high-level semantic information of entities by superimposing network layers. Design an attention aggregator at the prediction layer to refine the embedding representation of entities. Finally predict the user-recipe interaction probability. Experimental results prove the advancement and effectiveness of CGRS.

KEYWORDS:

• Recipe recommendation
• attention neural network
• collaborative knowledge propagation
• attention feature learning network
• recipe knowledge graph

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

Some data from the Ta-da dataset are used in this study and will be gradually opened after the completion of subsequent laboratory projects. The sample data is part of the Ta-da data set and can be accessed at https://github.com/Eimo-Bai/Ta-da-recipe-dataset, visited on December 17, 2022.

Additional information

Funding

The works described in this paper are supported by The National Natural Science Foundation of China under grant number 61802352; The Program for Young Key Teachers of Henan Province under grant number 2021GGJS095; The Project of Science and Technology in Henan Province: 232102210051; The Project of collaborative innovation in Zhengzhou under grant number 2021ZDPY0208; The National Natural Science Foundation of China under grant number 62102372;