An Integrated Framework for Contextual Personalized LLM-Based Food Recommendation
This work addresses the problem of ineffective food recommendations for users, though it appears incremental as it builds on existing RLP strategies with domain-specific adaptations.
The paper tackles the underperformance of personalized food recommendation systems by introducing the Food Recommendation as Language Processing (F-RLP) framework, which leverages large language models in a tailored way to provide contextual and personalized recommendations.
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 Language Models (LLMs) offer promise, current generic Recommendation as Language Processing (RLP) strategies lack the necessary specialization for the food domain's complexity. This thesis tackles these deficiencies by first identifying and analyzing the essential components for effective Food-RecSys. We introduce two key innovations: a multimedia food logging platform for rich contextual data acquisition and the World Food Atlas, enabling unique geolocation-based food analysis previously unavailable. Building on this foundation, we pioneer the Food Recommendation as Language Processing (F-RLP) framework - a novel, integrated approach specifically architected for the food domain. F-RLP leverages LLMs in a tailored manner, overcoming the limitations of generic models and providing a robust infrastructure for effective, contextual, and truly personalized food recommendations.