Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph

Chen, Yu; Subburathinam, Ananya; Chen, Ching-Hua; Zaki, Mohammed J.

doi:10.1145/3437963.3441816

Cited by 39 publications

(19 citation statements)

References 33 publications

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“…The core utility of the food recommendation system is to provide recipe retrieval ( 16 ). PFoodReq is a novel question-answering food recommendation system based on a large-scale food knowledge base/graph ( 17 ). In general, PFoodReq will follow the user's question, such as “What's good with bread for breakfast?”, and then output all recipes from the model.…”

Section: Resultsmentioning

confidence: 99%

Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review

2022

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Research on obesity and related diseases has received attention from government policymakers; interventions targeting nutrient intake, dietary patterns, and physical activity are deployed globally. An urgent issue now is how can we improve the efficiency of obesity research or obesity interventions. Currently, machine learning (ML) methods have been widely applied in obesity-related studies to detect obesity disease biomarkers or discover intervention strategies to optimize weight loss results. In addition, an open source of these algorithms is necessary to check the reproducibility of the research results. Furthermore, appropriate applications of these algorithms could greatly improve the efficiency of similar studies by other researchers. Here, we proposed a mini-review of several open-source ML algorithms, platforms, or related databases that are of particular interest or can be applied in the field of obesity research. We focus our topic on nutrition, environment and social factor, genetics or genomics, and microbiome-adopting ML algorithms.

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Section: Resultsmentioning

confidence: 99%

Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review

2022

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“…Only one part is shared during FL model aggregation, while the other part containing personalized parameters or even heterogeneous structures is held locally. FedRep [9], FedMatch [6], FedBABU [26] and FedAlt/FedSim [27] share the homogeneous feature extractor while LG-FedAvg [20], CHFL [22] and FedClassAvg [15] share the homogeneous classifier header. Since only part of a complete model is shared, model performance tends to degrade compared with sharing the complete model (e.g., FedAvg).…”

Section: Related Workmentioning

confidence: 99%

FedGH: Heterogeneous Federated Learning with Generalized Global Header

Yi¹,

Wang²,

Liu³

et al. 2023

Preprint

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Federated learning (FL) is an emerging machine learning paradigm that allows multiple parties to train a shared model collaboratively in a privacy-preserving manner. Existing horizontal FL methods generally assume that the FL server and clients hold the same model structure. However, due to system heterogeneity and the need for personalization, enabling clients to hold models with diverse structures has become an important direction. Existing model-heterogeneous FL approaches often require publicly available datasets and incur high communication and/or computational costs, which limit their performances. To address these limitations, we propose the Federated Global prediction Header (FedGH) approach. It is a communication and computation-efficient modelheterogeneous FL framework which trains a shared generalized global prediction header with representations extracted by heterogeneous extractors for clients' models at the FL server. The trained generalized global prediction header learns from different clients. The acquired global knowledge is then transferred to clients to substitute each client's local prediction header. We derive the nonconvex convergence rate of FedGH. Extensive experiments on two real-world datasets demonstrate that FedGH achieve significantly more advantageous performance in both model-homogeneous and -heterogeneous FL scenarios compared to seven state-of-the-art personalized FL models, beating the best-performing baseline by up to 8.87% (for model-homogeneous FL) and 1.83% (for modelheterogeneous FL) in terms of average test accuracy, while saving up to 85.53% of communication overhead.

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“…As one use case, personalized food recommendation is conducted over the constructed food knowledge graph FoodKG 19 with recipes, ingredients, and nutrients. 124 When providing a recommendation, given a user query (e.g., “What is a good lunch that contains meat?”) as the input, the system retrieves all recipes from FoodKG for the recommendation. Specifically, the system identifies the query type first.…”

Section: Applications Of Food Knowledge Graphsmentioning

confidence: 99%

Applications of knowledge graphs for food science and industry

Min

et al. 2022

Patterns

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Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph

Cited by 39 publications

References 33 publications

Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review

Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review

FedGH: Heterogeneous Federated Learning with Generalized Global Header

Applications of knowledge graphs for food science and industry

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