Anticancer Recipe Recommendation Based on Cancer Dietary Knowledge Graph

Many recipes contain ingredients with various anticancer effects, which can help users to prevent cancer, as well as provide treatment for cancer patients, effectively slowing the disease. Existing recipe knowledge graph recommendation systems obtain entity feature representations by mining latent c...

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Bibliographic Details
Published in:European journal of cancer care 2023-10, Vol.2023, p.1-13
Main Authors: Tang, Jianchen, Huang, Bing, Xie, Mingshan
Format: Article
Language:English
Subjects:
Algorithms
Anticancer properties
Cancer
Care and treatment
Chronic illnesses
Computational linguistics
Diet
Disease prevention
Embedding
Knowledge representation
Language processing
Medical research
Natural language interfaces
Prevention
Recipes
Recommender systems
System effectiveness
Taste
Taste preferences
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Summary:Many recipes contain ingredients with various anticancer effects, which can help users to prevent cancer, as well as provide treatment for cancer patients, effectively slowing the disease. Existing recipe knowledge graph recommendation systems obtain entity feature representations by mining latent connections between recipes and between users and recipes to enhance the performance of the recommendation system. However, it ignores the influence of time on user taste preferences, fails to capture the dependency between them from the user’s dietary records, and is unable to more accurately predict the user’s future recipes. We use the KGAT to obtain the embedding representation of entities, considering the influence of time on users, and recipe recommendation can be viewed as a long-term sequence prediction, introducing LSTM networks to dynamically adjust users’ personal taste preferences. Based on the user’s dietary records, we infer the user’s preference for the future diet. Combined with the cancer knowledge graph, we provide the user with diet recommendations that are beneficial to disease prevention and rehabilitation. To verify the effectiveness and rationality of PPKG, we compared it with three other recommendation algorithms on the self-created datasets, and the extensive experimental results demonstrate that our algorithm performance performs other algorithms, which confirmed the effectiveness of PPKG in dealing with sequence recommendation.
ISSN:1365-2354
0961-5423
1365-2354
DOI:10.1155/2023/8816960