Transforming iFood’s Content Classification: AI-Powered
Efficiency and Scalability with Large Language Models
Anna Castro

Murilo V. F. Menezes

iFood

iFood

anna.castro@ifood.com.br

murilo.menezes@ifood.com.br

ABSTRACTIn the online food delivery sector, effective product classification is crucial for maximizing user engagement and optimizing sales. In this context, data labeling is essential tomonitor and improve current classification models. Manual labeling, however, is both time-consuming and costly.
Leveraging Artificial Intelligence (AI) is pivotal in achieving
efficiency at scale. By using large language models (LLMs),iFood developed an AI-driven workflow to build high-precision,
high-coverage classifiers, and improve them with increased
team productivity. This presentation will explore our experience in deploying and maintaining multiple machine learning models at scale, as well as our usage of generative models
in our model’s lifecycle. We will discuss the process of building our offer curation platform, challenges we faced while integrating with generative models, and how using LLMs facilitated model monitoring, ultimately improving offer quality
on iFood. This research offers valuable insights into practical AI applications, with potential implications for improving classification processes across various industries.

1.

POTENTIAL DISCUSSION POINTS• Enhanced Model Performance: Explore how the utilization of multiple Machine Learning models has contributed to an increased precision and recall in offer classification, using our item taxonomy model as
the main use case. By automatizing data labelingusing large language models [1; 2], we streamlined
the process of labeling new data and creating newclasses, as well as identifying gaps in model performance. By better utilizing unlabeled data in conjunction with a smaller set of manually labeled data, we
can better capture the underlying structure of the data
and make more informed classification decisions, leading to a more refined taxonomy and reduced ambiguity
in offer categorization.
• Improved Offer Coverage: Discuss the effectiveness of
automatic labeling in reducing the percentage of items
classified with low confidence. Increasing our capacity to identify rare and diverse items, we were able
to increase the percentage of validly classified items,
improving our offer coverage inside the platform.
• Scalability and Efficiency: Investigate the scalability
and efficiency benefits of automatic labeling in offer

classification. By reducing the reliance on manuallylabeled data and leveraging the vast amount of unlabeled data available, LLM labeling enables organizations like iFood to scale their offer classification systems efficiently, handling large volumes of data while
maintaining high classification accuracy and reducing
operational costs.
• Model Monitoring: Discuss how LLMs can be used
to collect feedback on models’ performance, enablingteams and organizations to track metrics, such as accuracy and precision, on an unprecedented scale. Furthermore, we will talk about statistical techniques toassess the reliability of metric’s estimates.

2.

RELEVANCEThe pertinence of data labeling using generative models extends beyond the realm of offer classification, resonating
deeply within the broader context of supervised machinelearning and data mining. Its pragmatic application in realworld scenarios, exemplified by its utilization in refining offer
taxonomy within online food delivery platforms like iFood,underscores its transformative potential across diverse domains. By harnessing both labeled and unlabeled data, integrating LLMs to our model’s lifecycle offers a pragmatic
solution to the perennial challenge of data scarcity, particularly prevalent in industries where manually labeling vast
datasets is impractical or cost-prohibitive. This approach
not only enhances classification accuracy and reduces ambiguity but also fosters scalability and efficiency, paving theway for more agile and adaptive systems capable of handlinglarge volumes of data while minimizing human intervention.
Moreover, the insights gleaned from iFood’s implementation of large language models in offer classification resonate
deeply within the knowledge discovery and data mining (KDD)
community. They not only showcase the practical utility ofLLMs but also inspire further innovation and exploration inmethodology development and application. As researchersand practitioners seek to address the complex challenges
posed by real-world datasets, automatic labeling offers acompelling avenue for advancing the frontier of knowledge
discovery, enabling more nuanced insights, and empowering organizations to derive actionable intelligence from theirdata. Thus, iFood’s experience serves as a beacon, illuminating the path toward harnessing the full potential of large
language models and generative AI in revolutionizing content classification processes and driving meaningful advancements in machine learning and data mining.

3.

THE PRESENTER

Murilo V. F. Menezes is a Staff Data Scientist at iFood,
Brazil’s leading food delivery company. Murilo acts as aTech Lead on iFood’s catalog understanding platform, andwas directly involved in its conception and development. He
holds a Master’s in Machine Learning from Federal University of Minas Gerais (UFMG), actively working as an associate researcher in kernel methods, representation learning,and pattern classification.

4.

THE COMPANY

iFood is the most popular delivery app in Brazil that connects customers with a wide range of businesses, including
restaurants, supermarkets, pharmacies, and more, enablingusers to order meals and groceries for delivery. With over
80 million orders per month, 300 million items and 300,000
merchants across 1,700 cities, iFood’s challenges include pinpointing and tailoring offers for a large and extremely diverse
user base.

5.

REFERENCES[1] Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya,I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman,S., Anadkat, S., et al.: Gpt-4 technical report. arXiv
preprint arXiv:2303.08774 (2023)[2] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan,J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry,G., Askell, A., et al.: Language models are few-shotlearners. Advances in neural information processing systems 33, 1877–1901 (2020)