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 struggle with
problems characterized by an almost infinite number of classes and a limited
number of samples within an unbalanced dataset. Conversely, the emergence of
Large Language Models (LLMs) as recommendation engines offers a promising
avenue. However, a general-purpose Recommendation as Language Processing (RLP)
approach lacks the critical components necessary for effective food
recommendations. To address this gap, we introduce Food Recommendation as
Language Processing (F-RLP), a novel framework that offers a food-specific,
tailored infrastructure. F-RLP leverages the capabilities of LLMs to maximize
their potential, thereby paving the way for more accurate, personalized food
recommendations.

以大型语言模型 (Large Language Models) 为基础的食品推荐系统 (F-RLP) 通过食品推荐的语言处理方法 (Recommendation as Language Processing) 解决了传统基于规则和分类的食品推荐系统面临的问题，提供更准确、个性化的食品推荐。

食品推荐作为语言处理（F-RLP）：个性化和上下文范式

Food Recommendation as Language Processing (F-RLP): A Personalized and  Contextual Paradigm

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 learning ability
and generating responses given user queries leveraging knowledge obtained by
retrieval augmentation. One problem of deploying commercial retrieval-augmented
LLMs is the cost due to the additionally retrieved context that largely
increases the input token size of the LLMs. To mitigate this, we propose a
token compression scheme that includes two methods: summarization compression
and semantic compression. The first method applies a T5-based model that is
fine-tuned by datasets generated using self-instruct containing samples with
varying lengths and reduce token size by doing summarization. The second method
further compresses the token size by removing words with lower impact on the
semantic. In order to adequately evaluate the effectiveness of the proposed
methods, we propose and utilize a dataset called Food-Recommendation DB (FRDB)
focusing on food recommendation for women around pregnancy period or infants.
Our summarization compression can reduce 65% of the retrieval token size with
further 0.3% improvement on the accuracy; semantic compression provides a more
flexible way to trade-off the token size with performance, for which we can
reduce the token size by 20% with only 1.6% of accuracy drop.

使用自学生成的样本进行 Fine-tune，我们提出了两种方法进行文本压缩：摘要压缩和语义压缩。摘要压缩可以减少 65% 的检索令牌大小并提高 0.3% 的准确性，语义压缩可以将令牌大小减少 20%，但只有 1.6% 的准确性损失。

TCRA-LLM：基于令牌压缩检索的大规模语言模型，用于推理成本降低

TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for  Inference Cost Reduction

Food recommendation has become an important means to help guide users to
adopt healthy dietary habits. Previous works on food recommendation either i)
fail to consider users' explicit requirements, ii) ignore crucial health
factors (e.g., allergies and nutrition needs), or iii) do not utilize the rich
food knowledge for recommending healthy recipes. To address these limitations,
we propose a novel problem formulation for food recommendation, modeling this
task as constrained question answering over a large-scale food knowledge
base/graph (KBQA). Besides the requirements from the user query, personalized
requirements from the user's dietary preferences and health guidelines are
handled in a unified way as additional constraints to the QA system. To
validate this idea, we create a QA style dataset for personalized food
recommendation based on a large-scale food knowledge graph and health
guidelines. Furthermore, we propose a KBQA-based personalized food
recommendation framework which is equipped with novel techniques for handling
negations and numerical comparisons in the queries. Experimental results on the
benchmark show that our approach significantly outperforms non-personalized
counterparts (average 59.7% absolute improvement across various evaluation
metrics), and is able to recommend more relevant and healthier recipes.

提出了一种基于大规模食品知识图谱和健康指南的个性化饮食推荐框架，将用户查询、饮食偏好和健康指南作为自然语言问答系统的约束条件，用于促进用户养成健康的饮食习惯。

基于大规模食品知识图谱的个性化食品推荐

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

A growing proportion of the global population is becoming overweight or
obese, leading to various diseases (e.g., diabetes, ischemic heart disease and
even cancer) due to unhealthy eating patterns, such as increased intake of food
with high energy and high fat. Food recommendation is of paramount importance
to alleviate this problem. Unfortunately, modern multimedia research has
enhanced the performance and experience of multimedia recommendation in many
fields such as movies and POI, yet largely lags in the food domain. This
article proposes a unified framework for food recommendation, and identifies
main issues affecting food recommendation including building the personal
model, analyzing unique food characteristics, incorporating various context and
domain knowledge. We then review existing solutions for these issues, and
finally elaborate research challenges and future directions in this field. To
our knowledge, this is the first survey that targets the study of food
recommendation in the multimedia field and offers a collection of research
studies and technologies to benefit researchers in this field.

提出了一个用于食物推荐的统一框架，并指出影响食物推荐的主要问题，包括构建个人模型、分析独特的食品特征、整合各种上下文和领域知识。本文回顾了现有解决方案，阐述了这个领域的研究挑战和未来方向。