In the last decade, recommendation systems have gone from being a simple content filter to becoming the growth engine of digital platforms, social networks and marketplaces. However, the traditional focus on short-term metrics—such as clicks, views, or immediate interactions—has proven to generate a paradox: users consume more, but fatigue faster and end up abandoning the service. The academic article that gives rise to this reflection proposes a paradigm shift towards the optimization of engagement in the long term through model-agnostic rewards, that is, signals that any learning system can interpret without the need to redesign its internal architecture.
The great difficulty of measuring and optimizing retention lies in the fact that the signals of a user's return – such as their return to the service days later – are extremely dispersed, arrive late and we can hardly attribute them to a specific recommendation. While advanced techniques such as reinforcement learning or sequential models exist, they often require specific reward engineering, high computational cost, and make generalization difficult. In view of this, the agnostic approach proposes to identify, through offline screening, early behaviors within a session that are predictive of future retention. Rewards can be derived from observable patterns such as diversity of interactions, depth of navigation, or combination of actions on different surfaces.
For companies that operate with large volumes of data, implementing this type of optimization poses a significant technical challenge. It requires robust infrastructure, real-time data pipelines, and machine learning models capable of integrating these new signals without destabilizing existing systems. This is where the accompaniment of a specialized team makes the difference. At Q2BSTUDIO, as a software and technology development company, we have accompanied multiple organizations in the transformation of their recommendation systems towards a long-term vision. Our AI services for enterprises enable the design of architectures that learn continuously, while our bespoke application solutions make it easy to integrate these capabilities into production environments.
The key to making an agnostic reward work in production is in feature engineering: you need to transform user behavior into numerical signals that any model can consume. For example, instead of optimizing only the immediate click, you can weigh the likelihood that an interaction will lead to a longer session or a subsequent visit. The landmark paper highlights that an offline screening framework can identify which early behaviors are truly predictive — and discard those that only generate noise. This process, although conceptually simple, requires a strong component of experimentation and a scalable cloud platform. The AWS and Azure cloud solutions we offer at Q2BSTUDIO provide the flexibility to run massive A/B tests, store large volumes of event data, and deploy models efficiently.
In addition, adopting an agnostic approach has direct implications for cybersecurity and privacy, since by working with aggregated signals and not with sensitive user data, the exposure surface is reduced. Our cybersecurity team helps companies audit these pipelines to ensure that no personal data is exposed during the learning process.
From a business perspective, optimizing retention rather than short-term click completely changes success metrics. Product teams are starting to measure user value (LTV) and design experiences that encourage exploration. Agnostic rewards allow models to learn how to recommend content that is not only relevant, but also diverse and surprising, because the diversity of early actions often correlates with a higher return. In practice, this translates into a decrease in the dropout rate and an increase in perceived satisfaction.
To implement this type of system in an organization, it is not enough to have a data science team. Careful orchestration of data engineering, cloud infrastructure, and software development is needed. The business intelligence services that we implement in Q2BSTUDIO, such as dashboards based on Power BI, allow you to visualize in real time how retention and engagement metrics are evolving, facilitating strategic decision-making. In addition, automating model update processes using AI agents reduces manual intervention and accelerates the continuous improvement cycle.
One aspect that is often overlooked is the need for bespoke software to connect recommendation systems with each company's proprietary data sources. Not all platforms can use pre-packaged models; Many require specific adaptations to integrate signals from multiple surfaces – such as the main feed, searches or notifications. Our experience in custom application development has allowed us to build middleware that enriches user events with temporary features before feeding into the model, ensuring that agnostic rewards are calculated accurately and with low latency.
In short, the future of recommendation systems lies in abandoning the obsession with the immediate click and embracing a holistic vision of user value. Agnostic rewards offer a practical and scalable path to achieve this, supported by cloud infrastructure, artificial intelligence, and robust software engineering. At Q2BSTUDIO, we help companies navigate that path by combining technical expertise, agile methodology, and deep business knowledge. If your platform is looking to improve long-term retention without compromising current performance, we can collaborate on the design and implementation of a framework tailored to your needs.
