Papers Food recommendation
“Food recommendation” 태그가 달린 논문 31편 · 필터 해제
MORL-A2C: Multi-Objective Reinforcement Learning Reranker for Optimizing Healthiness in MOPI-HFRS
Unhealthy dietary behavior continues to be a persistent public health issue in the United States, exacerbated by recommendation systems that prioritize user preference without considering nutritional health. The Multi-Ob…
Reinforcement LearningRecommendation SystemsFood recommendationMetaPlate: Counterfactual-Guided RAG-LLM Tool for Personalized Food Recommendation and Hyperglycemia Prevention
Postprandial hyperglycemia is a key risk factor for metabolic disorders; however, existing dietary guidance is often static, impractical, and insufficiently personalized, providing recommendations that are difficult to f…
Recommendation SystemsFood recommendationAn LLM-RAG Approach for Healthy Eating Index-Informed Personalized Food Recommendations
Diet quality is a leading determinant of chronic disease risk. Advances in artificial intelligence (AI) have enabled food recommendation systems to adapt suggestions to user preferences and health goals. However, most cu…
Food recommendationKERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models
Recent advances in large language models (LLMs) and the abundance of food data have resulted in studies to improve food understanding using LLMs. Despite several recommendation systems utilizing LLMs and Knowledge Graphs…
Food recommendationKnowledge GraphsRecipe GenerationRecommendation SystemsAn Integrated Framework for Contextual Personalized LLM-Based Food Recommendation
Personalized food recommendation systems (Food-RecSys) critically underperform due to fragmented component understanding and the failure of conventional machine learning with vast, imbalanced food data. While Large Langu…
Food recommendationRecommendation SystemsFood Recommendation With Balancing Comfort and Curiosity
Food is a key pleasure of traveling, but travelers face a trade-off between exploring curious new local food and choosing comfortable, familiar options. This creates demand for personalized recommendation systems that ba…
Density EstimationFood recommendationRecommendation SystemsMOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation
The prevalence of unhealthy eating habits has become an increasingly concerning issue in the United States. However, major food recommendation platforms (e.g., Yelp) continue to prioritize users' dietary preferences over…
DescriptiveFood recommendationGraph LearningLarge Language Model+1Multi-modal Food Recommendation using Clustering and Self-supervised Learning
Food recommendation systems serve as pivotal components in the realm of digital lifestyle services, designed to assist users in discovering recipes and food items that resonate with their unique dietary predilections. Ty…
ClusteringFood recommendationRecommendation SystemsSelf-Supervised LearningChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework
The profound impact of food on health necessitates advanced nutrition-oriented food recommendation services. Conventional methods often lack the crucial elements of personalization, explainability, and interactivity. Whi…
Causal DiscoveryFood recommendationNutritionFood Recommendation as Language Processing (F-RLP): A Personalized and Contextual Paradigm
State-of-the-art rule-based and classification-based food recommendation systems face significant challenges in becoming practical and useful. This difficulty arises primarily because most machine learning models struggl…
Food recommendationRecommendation SystemsTCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction
Since ChatGPT released its API for public use, the number of applications built on top of commercial large language models (LLMs) increase exponentially. One popular usage of such models is leveraging its in-context lear…
Food recommendationIn-Context LearningLanguage ModelingLanguage Modelling+3SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection
Food detection is becoming a fundamental task in food computing that supports various multimedia applications, including food recommendation and dietary monitoring. To deal with real-world scenarios, food detection needs…
DenoisingFood recommendationGeneralized Zero-Shot Object DetectionGeneralized Zero-Shot Object Detection on MS-COCO+2Explainable Active Learning for Preference Elicitation
Gaining insights into the preferences of new users and subsequently personalizing recommendations necessitate managing user interactions intelligently, namely, posing pertinent questions to elicit valuable information ef…
Active LearningFood recommendationTBIN: Modeling Long Textual Behavior Data for CTR Prediction
Click-through rate (CTR) prediction plays a pivotal role in the success of recommendations. Inspired by the recent thriving of language models (LMs), a surge of works improve prediction by organizing user behavior data i…
ChunkingClick-Through Rate PredictionFood recommendationPredictionExploring the Spatiotemporal Features of Online Food Recommendation Service
Online Food Recommendation Service (OFRS) has remarkable spatiotemporal characteristics and the advantage of being able to conveniently satisfy users' needs in a timely manner. There have been a variety of studies that h…
Food recommendationCook-Gen: Robust Generative Modeling of Cooking Actions from Recipes
As people become more aware of their food choices, food computation models have become increasingly popular in assisting people in maintaining healthy eating habits. For example, food recommendation systems analyze recip…
Food recommendationNutritionRecommendation SystemsDish detection in food platters: A framework for automated diet logging and nutrition management
Diet is central to the epidemic of lifestyle disorders. Accurate and effortless diet logging is one of the significant bottlenecks for effective diet management and calorie restriction. Dish detection from food platters …
Food recommendationManagementMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4Advances in Automatically Rating the Trustworthiness of Text Processing Services
AI services are known to have unstable behavior when subjected to changes in data, models or users. Such behaviors, whether triggered by omission or commission, lead to trust issues when AI works with humans. The current…
Food recommendationMenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model
Food recommendation system has proven as an effective technology to provide guidance on dietary choices, and this is especially important for patients suffering from chronic diseases. Unlike other multimedia recommendati…
Food recommendationLearning-To-RankOptical Character RecognitionOptical Character Recognition (OCR)Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety
AI systems have been widely adopted across various domains in the real world. However, in high-value, sensitive, or safety-critical applications such as self-management for personalized health or food recommendation with…
Food recommendationManagement