iFood Enhances Recommendation Systems and Ad Performance with Kumo AI

Kumo has the potential to accelerate other projects within iFood. It was easier and faster than creating a model from scratch and gave us high quality recommendations.
– Luiz Mendes, Head of Data Science at iFood

iFood is the largest food delivery company in Brazil, known for its extensive range of services, including groceries and payment solutions, in addition to food delivery. Handling over 80 million orders per month, iFood has established itself as a dominant player in the Brazilian food delivery market.

iFood partnered with Kumo AI to address its challenges in scaling its recommendation systems, managing high computational costs, and delivering personalized, context-aware recommendations.

Kumo offered a sophisticated solution that seamlessly integrated with iFood’s existing infrastructure, which included Amazon S3 as its central data storage, various data processing pipelines, and multiple machine learning platforms. This integration allowed iFood to improve recommendation relevance and effectiveness while handling the vast scale of operations.

CHALLENGES

1. Scaling Recommendation Systems: Recommendation was one of the first AI challenges that iFood tackled around 2019. Initially, iFood created an online matrix-based model that worked well for a while. However, as iFood grew, this online-only approach did not scale or perform as expected. iFood then invested in creating a robust recommendation platform to address scalability issues and evolve its recommendation capabilities. Luiz Mendes, Head of Data Science at iFood, highlighted the complexity of maintaining accurate and relevant recommendations at such a large scale. “Managing the vast amount of data and providing real-time, personalized recommendations was a significant challenge,” Mendes explained.

In the beginning, iFood sought to overcome several challenges with its recommendation system:

• Resource Intensity: The initial online-only matrix-based model required significant computational resources, which increased operational costs and complexity.
• Optimization: Early optimizations improved efficiency but couldn’t fully capture the intricate patterns and relationships in the data required for highly personalized recommendations.
• Real-Time Processing: Providing real-time recommendations added an additional layer of complexity that the early models and infrastructure struggled to manage effectively.

2. Personalized and Context-Aware Recommendations: iFood aimed to deliver personalized recommendations for food orders, considering the context such as time of day, user preferences, and location. This level of personalization is complex due to the diverse user base and varying preferences. Mendes highlighted the importance of providing relevant suggestions to enhance user experience and engagement. “The diversity in our user base, with different tastes and preferences, made it crucial to offer recommendations that would be universally relevant and engaging.”.

To achieve this, iFood employed various methods to personalize recommendations, such as using collaborative filtering techniques and incorporating user feedback. They also experimented with different contextual factors, like time of day, user location, and historical ordering patterns, to refine their suggestions. These efforts included:

• Data Diversity: The vast and varied user data made it challenging to create a one-size-fits-all recommendation system.
• Contextual Complexity: Incorporating factors such as time of day and user location, but this integration often requires increased processing power and additional storage. For instance, a user’s food preference might change depending on whether they are ordering lunch at work or dinner at home.
• Scalability Issues:  To manage scalability and provide real-time recommendations effectively, iFood employs an innovative offline-online approach. In this strategy, the more complex models operate offline, processing a curated set of candidates, while a faster model reranks these candidates in real time to ensure timely and relevant recommendations. Despite enhancing the system’s responsiveness and efficiency, maintaining some of these sophisticated offline models has proven to be costly and challenging. Integrating Kumo into this existing setup proved seamless, adding substantial value without any complications. This smooth integration underscores Kumo’s compatibility and ease of deployment within our advanced recommendation architecture..

Luiz Mendes highlighted the complexity of iFood’s recommendation challenges, noting, ‘While our team has made significant strides in incorporating diverse data points and contextual factors, we are always looking for ways to further enhance our models. Kumo provided us with an advanced AI solution that complemented our existing efforts, helping us tackle intricate relationships within our data more effectively.’

3. High Computational Costs: Maintaining and updating machine learning models involves high computational costs and complexity. The data science team at iFood faced challenges in managing the computational resources required to run its models, leading to increased operational costs and the need for optimization.

To manage high computational costs, iFood focused on optimizing its algorithms and models to reduce resource consumption. This involved tuning hyperparameters, simplifying model architectures, and utilizing more efficient data processing techniques. Despite these efforts, the computational demands remained significant, necessitating further innovation and optimization.

SOLUTION

Kumo provided iFood with a sophisticated AI solution to address its recommendation system challenges. Here’s how Kumo’s capabilities transformed iFood’s operations:

Enhancing Recommendation Systems: Kumo introduced advanced predictive models to enhance iFood’s recommendation systems. These models effectively handled the complexity and scale of iFood’s data, enabling more accurate and context-aware ad targeting. By analyzing relationships between users and items, Kumo’s AI could predict user behavior and preferences with greater precision.

Integration with Existing Infrastructure: Kumo’s team worked closely with iFood’s data scientists to ensure a seamless transition. The process involved configuring Kumo AI with iFood’s data stored on Amazon S3 and their existing data processing pipelines. As Luiz Mendes mentioned, “The integration with S3 makes it much easier than having to build integration between platforms and keeping it updated.” Regular meetings and feedback sessions facilitated a smooth integration, addressing any issues promptly and ensuring optimal performance.

Specific Features and Capabilities of Kumo’s Technology:

• Eliminating Feature Engineering: Kumo automated much of the feature engineering process by learning embeddings directly from the data, significantly reducing the time and effort required from iFood’s data science team. Luiz explained, “Using Kumo greatly improved our experience with feature engineering over traditional models. By reducing the time and effort required from our team, we were able to develop an initial functional and acceptable MVP much faster.”
• Optimizing Computational Costs: “iFood had a graph-based recommender system that was our most costly model, requiring numerous machines with GPUs to train and serve. Kumo’s technology helped optimize computational costs by streamlining the data processing and model training processes,” Luiz noted.  “Kumo replaced significant parts of our process, allowing us to focus on creating ETL outputs. This optimization has streamlined our offline processes.”

Results and Impact

The integration of Kumo’s AI capabilities yielded significant improvements in iFood’s recommendation systems and ad performance. Here are the key outcomes based on the conversation with Luiz Mendes:

Improved Recommendation Accuracy: Kumo’s advanced algorithms brought a substantial boost to iFood’s recommendation accuracy. The components that utilized Kumo’s model experienced conversion rate increases ranging from 1% (for already high-performing components) to 10% (for less popular components), demonstrating Kumo’s ability to drive significant engagement across various scenarios. Notably, users began relying more on recommendations instead of the search and last-orders components, reflecting improved user satisfaction. 

Enhanced User Engagement and Conversion Rates: The more accurate and context-aware recommendations resulted in higher user engagement. Users were more likely to find relevant food and restaurant suggestions, leading to increased order volumes and higher conversion rates. The personalized recommendations fostered a more engaging and satisfying user experience, driving repeat usage and customer loyalty. iFood saw a remarkable increase in ad performance, with click-through rates (CTR) improving by up to 2% and ad conversions rising by approximately 3%. These enhancements ensured that users received more relevant food and restaurant suggestions, significantly elevating their overall experience on the platform.

Increased Revenue: The improvements in recommendation accuracy, user engagement, and conversion rates collectively contributed to an increase in iFood’s revenue. By providing more relevant and timely recommendations, iFood was able to drive higher sales and improve customer satisfaction. The enhanced recommendations not only boosted order volumes but also encouraged users to explore and purchase more frequently.

Looking forward, iFood aims to continue leveraging Kumo’s AI capabilities to further enhance its recommendation systems, explore new use cases, and maintain its position as a leader in the Brazilian food delivery market. The collaboration between iFood and Kumo underscores the potential of advanced AI technologies in transforming business operations and achieving strategic goals.

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