UC Irvine

Personalized Food Recommendation Systems (Food-RecSys) are recognized for their potential to enhance dietary practices and address pressing health issues. Despite their promise, current food recommendation methodologies, predominantly rule-based and classification-driven, encounter substantial barriers in achieving practical applicability. This challenge is largely attributed to two main factors: the poor understanding of food recommendation specific components as a whole and inadequate research on a holistic integration between all the key components which enables the final methodology to achieve effective results, and also, the limitations of conventional machine learning models, which falter in the face of a virtually limitless array of food categories and the inherent imbalance within datasets. In this context, the advent of Large Language Models (LLMs) presents an intriguing alternative, suggesting a path forward for recommendation systems. Nevertheless, existing approaches that employ LLMs generally adopt a generic Recommendation as Language Processing (RLP) strategy, which falls short of incorporating the nuanced components essential for successful Food-RecSys.

To bridge this research gap, this thesis provides a comprehensive overview on an intensely complex big picture, identifies the key components and provides innovative exploratory models for each the multiplex key components required for personalized contextual food recommendation, additionally, articulates a novel feasibility study of a comprehensive integrated personal contextual Food-RecSys framework, underpinned by several pivotal innovations aimed at fortifying the effectiveness of personalized food recommendations. Key among these is the development of a multimedia food logging platform and the World Food Atlas, the latter of which facilitates geolocation-based food queries—a capability notably absent in current offerings. Moreover, this work pioneers the Food Recommendation as Language Processing (F-RLP) framework. F-RLP represents a bespoke solution that adeptly harnesses the strengths of LLMs, furnishing a food-specific recommendation infrastructure that transcends the limitations of generic models.