Building real-time AI personalization experiences

“Hi [Insert name]”

In the early days of the internet, this was the first form of personalization. Services like email, social media, or mobile apps gave users the sense of being recognized through custom messages or welcome notifications using their names. Then, companies like Amazon, Netflix, and Facebook led the way in making customers feel seen. But today, personalization expectations are much higher.

Your users are more interactive than ever—they click, hover, search, and spend time engaging with content, generating useful data in just a few seconds. This data can reveal a lot about who they are and what they want from your business.

In this current digital age, data is viewed as one of the most valuable resources, even as a new currency. Companies are now using data to learn about how users act, what they like, and how they think. This is because of ‌progress made in AI, where models are getting faster, stronger, and more reliable.

Today, companies are looking to deliver personalized experiences that improve user satisfaction and drive sales and long-term loyalty. According to Accenture, 91% of consumers are more likely to shop with brands that recognize, remember, and offer relevant recommendations. This shift has led to a race among businesses to collect and analyze user data, using it to differentiate themselves in a crowded marketplace.

Data keeps growing every day. Just a few seconds of user interactions within a website or app can tell us who the user is, what they are looking for, and what they might like. We can predict future user behavior and needs with this data.

A report by Segment found that 69% of customers want a consistent and personalized experience across all channels, but few companies meet these demands. Whether it's planning for the next product a user might want to buy or suggesting content that matches their interests, personalization is now essential to modern business strategies. It's not just an option—it's a key part of modern business plans.

Real-time personalization isn’t just a luxury—it’s becoming a necessity for companies that want to thrive in today’s marketplace.

This is where recommendation systems make a difference. In short, they allow businesses to suggest things or content to the user based on how they use the service those businesses offer. However, until the last few years, recommendation systems heavily relied on large datasets and historical data, which often required time to process and implement. In a world where users make decisions in seconds, businesses need to do more than just give good suggestions. They need to give these recommendations in real time, making the user experience more personal.

Real-time personalization is the game-changer here. Unlike systems that predict the future based on past data, real-time personalization changes quickly to the user's current behavior. It offers relevant content or product suggestions while the user is still using the interface. As more businesses such as Amazon or Netflix started recognizing the value of real-time personalized experiences, this approach has become a key differentiator in a competitive market.

Yet, delivering real-time personalized experiences comes with a lot of complex challenges. For businesses to successfully implement this approach, data must be processed on the fly and delivered within seconds.

Beyond real-time capabilities, developers must also consider the broader infrastructure. What are the limitations and advantages of AI real-time personalization? How should data be collected? How to optimize the data collection, processing, streaming, and delivery via API for better efficiency without affecting the user experience? Another important factor is to make sure the data is clean. Bad data can lead to suggestions that are not relevant or accurate, which can frustrate users instead of making them happy.

This whitepaper explores these challenges and offers insights into how real-time AI personalization can drive user engagement and business success, and how to implement such a system. With the right architecture, today’s AI models, and best practices, businesses can harness the power of real-time data to offer experiences that stand out in a competitive, fast-paced digital world. Real-time personalization isn’t just a luxury—it’s becoming a necessity for companies that want to thrive in today’s marketplace.

Data keeps growing every day. Just a few seconds of user interactions within a website or app can tell us who the user is, what they are looking for, and what they might like.

 

Analyzing user interactions to gain insights into behavior and preferences

Businesses rely heavily on big data to make informed decisions, predict user behavior, and craft personalized experiences. Big data involves the collection and analysis of vast amounts of structured and unstructured information from various sources, including user interactions, browsing patterns, and transactions.

By leveraging advanced analytics, machine learning, and deep learning, companies can uncover deep insights into user behavior, preferences, and decision-making processes in real-time.

The three Vs of Big Data

To understand and utilize big data, organizations must consider three critical factors: volume, velocity, and variety:

• Volume:
  The sheer scale of data generated from billions of daily interactions requires scalable storage and processing solutions, often supported by distributed systems like Hadoop or Apache Spark
• Velocity:
  Speed is essential in real-time scenarios. Technologies like Apache Kafka and Apache Flink enable low-latency streaming, providing businesses with immediate insights and allowing for prompt actions
• Variety:
  Real-time personalization systems must handle diverse data types, from clickstream logs to social media interactions. Advanced techniques like Natural Language Processing (NLP), image recognition, and behavioral analytics help extract meaningful insights from heterogeneous data

By combining these technologies, businesses will gain granular insights–not just about what products users buy or content they show interest in, but how they interact with the platform, what content they engage with, and how long they spend on particular actions.

Big data helps companies like Netflix, Instagram, and Amazon improve user experience and drive sales. This is how they accomplish this:

Netflix: Personalizing user experience through data

Netflix uses big data and AI models to deliver personalized recommendations, with 
80% of the content users watch coming from its recommendation system. By collecting and analyzing data such as viewing habits, search history, and even the time of day users watch content, Netflix engineers can predict what viewers will likely enjoy next. This highly effective targeted approach creates over 250 million personalized experiences daily, allowing the application to tailor its content delivery to each user’s preferences.

Netflix’s personalization system is powered by a combination of AWS tools such as Amazon S3 for scalable content storage, EC2 for streaming, DynamoDB for fast data access, and SageMaker for machine learning to predict viewing preferences. These components enable Netflix to offer an evolving set of recommendations that keep users engaged and help them discover new content that aligns with their changing tastes.

Instagram: Personalized feeds based on user engagement

Instagram leverages AI technologies to offer personalized content by analyzing user interactions like comments, likes, and posts viewed. More than 20% of the content in Instagram feeds comes from accounts users don’t follow, which is possible because AI recommendations predict what might interest them based on past engagement. For example, a user who frequently interacts with fitness-related posts will be shown more similar content, including suggestions from creators, new accounts or communities.

It doesn’t only improve user satisfaction by delivering more relevant content but also helps creators expand their reach. Instagram’s AI-powered recommendations allow users to discover new interests while providing content creators with more visibility, thus enhancing the overall user experience.

Amazon: AI-powered recommendation engines to enhance shopping

Amazon’s recommendation engine is the cornerstone of its ecommerce success, driving around 35% of its sales. By analyzing customer behavior such as browsing history, previous purchases, and items added to the cart, Amazon predicts what products users are most likely to buy next. Amazon not only suggests related items but also provides cross-selling recommendations, like showing accessories that complement a product the user is already considering. For example, if the user is purchasing a PS5 Spider-man theme, Amazon might suggest games in the same genre as Spider-man, in this case, action games.

The company’s recommendation system is powered by Amazon Personalize, a fully managed machine learning service that enables real-time and batch recommendations. Amazon Personalize works by collecting user data, including events like views and purchases, to build a customized machine-learning model tailored to specific business needs. Once trained, the model is deployed and accessed through APIs, ensuring that recommendations are contextually relevant and optimized for real-time interactions.

This AI-driven personalization allows users to discover relevant products quickly, improving the shopping experience and boosting overall order value.

 

User satisfaction and business benefits

Real-time personalization is no longer a luxury; it is essential for improving user satisfaction and driving measurable business outcomes. As users now expect tailored interactions, businesses that can deliver relevant content and recommendations see considerable improvements in engagement, retention, and revenue.

Improved user satisfaction

Real-time personalization delivers more relevant content, allowing users to find what they need without unnecessary friction. According to Epsilon, 80% of consumers are more likely to purchase from brands that provide personalized experiences. Tailored recommendations streamline user interactions, improving overall satisfaction by reducing the time and effort required to navigate an application.

Business advantages

Personalized experiences not only improve satisfaction but also contribute directly to key business metrics. HubSpot reports that personalized calls-to-action have a 202% higher conversion rate than generic alternatives. By offering personalized recommendations, businesses can significantly improve Average Order Value (AOV) and Conversion Rate (CR).

The impact of personalized product recommendations on Average Order Value (AOV). Source: Barillance

AOV growth 

As shown in the chart below, increased interaction with personalized recommendations leads to a substantial rise in AOV, climbing from $44.41 to over $400. Personalized product suggestions encourage users to make larger purchases by offering relevant upsells and cross-sells.

CR improvement
The data also highlights a clear correlation between recommendation engagement and conversion rate, which increases from 1.02% to 8.57%. Personalization reduces decision friction, making it easier for users to convert.

The correlation between recommendation engagement and conversion rate (CR). Source: Barillance

By implementing real-time personalization, businesses can drive higher satisfaction, increase conversions, and foster long-term loyalty, all of which translate into meaningful revenue growth.

AI real-time personalization and its importance

As users demand hyper-relevant experiences, real-time personalization lets companies deliver content that aligns with users' current needs. This instant responsiveness sets businesses apart in a crowded market, boosting customer loyalty and trust.

With the rapid development of AI tools, machine and deep learning algorithms, storage, and programming languages, implementing real-time personalization should sound rather straightforward. Implementing real-time AI personalization involves several technical challenges. Here are the key questions you should consider as a developer or tech leader:

• How can data be collected at low latency for scalable real-time data processing?
• Why choose real-time personalization instead of traditional recommendation systems?
• Which machine learning models are best suited for real-time personalization, and how can they be optimized to avoid performance issues?
• What strategies can address cold start challenges that become more pronounced with real-time personalization?
• What are the main challenges in maintaining low-latency pipelines for real-time personalization?
• How can real-time systems prevent overwhelming users with excessive recommendations?
• What approaches can mitigate risks such as biases in real-time personalization systems?
• What is an effective framework for implementing the process of real-time personalization?

By addressing these challenges, you can create more effective real-time personalization systems that deliver value not just to users, but to the entire organization. 

The incoming sections will delve deeper into these questions, exploring deeply the technical and strategic elements involved in implementing AI real-time personalization effectively.

Why real-time personalization?

We have seen that real-time personalization is a powerful tool that greatly boosts metrics like AOV, conversion rates, and user satisfaction. However, there’s often confusion between real-time personalization and simple recommendation systems.

It’s important to understand that both have the main objective of improving user experiences, but they work differently and are suited to distinct scenarios.

Here, we will define each approach, mention the technologies and algorithms used behind them, and discuss in which case to choose one over the other.

Defining real-time personalization and historical recommendation systems

Real-time personalization adapts the user experience based on current interactions, while historical recommendation systems rely on past user behavior to generate insights. This distinction suggests that recommendation is a subset of personalization, as personalization leverages real-time data to adjust the user interface to evolving preferences.

Now that we’ve clarified the differences between real-time personalization and historical recommendation systems, let’s explore the key technologies behind historical recommendation systems.

 

Technologies behind real-time personalization

Implementing a real-time personalization system requires low-latency technologies designed for instant responsiveness.

Data streaming tools like Apache Kafka and AWS Kinesis enable real-time processing of user interactions as events, allowing systems to dynamically adapt to user behavior.
In-memory databases such as Redis and Hazelcast provide fast session and profile storage, essential for rapid data retrieval and modification in low-latency applications, though careful handling is necessary to manage memory invalidation and stale data.

Machine learning models for real-time inference are deployed with platforms like TensorFlow Serving or AWS SageMaker, supporting algorithms like Contextual Bandits for immediate predictions; custom models or tools like Vowpal Wabbit can also be utilized for highly specific needs.

Finally, an event-driven architecture based on microservices ensures each user interaction triggers independent service responses, with WebSockets, notifications, or periodic polling used to update the frontend in real-time, ensuring a seamless, personalized user experience.

Technologies behind recommendation systems

A simple recommendation system requires a robust data infrastructure, beginning with data warehouses like Amazon Redshift, Google BigQuery, or Snowflake, which converge high-volume datasets including user logs, ratings, and interaction histories. ETL pipelines (e.g., AWS Glue, Apache NiFi) transform raw data into structured formats, ensuring optimal storage and retrieval, while distributed batch processing frameworks such as Apache Hadoop and Spark execute large-scale data transformations and periodic model updates.

Core algorithms include content filtering, which targets items based on prior user interactions, and collaborative filtering that leverages matrix factorization to model preferences across similar user profiles, facilitating precise, data-driven recommendations.

Other machine-learning models for recommendation algorithms also include neural networks and k-means clustering.

Architecture
The typical architecture features data sources feeding into an ETL pipeline that processes and stores information in a data warehouse. Batch processing frameworks then analyze this data and update recommendation models. An API layer accesses the model predictions to provide insights during user interactions.

Netflix employs a similar architecture, storing user interaction data, like watch histories, in Amazon S3 and Apache Cassandra, and using Apache Spark for daily processing. They deploy these models with Docker and Kubernetes to generate personalized content suggestions based on historical interactions.

We now have a clear idea about the common technologies behind each approach, the next step is determining when to use real-time personalization or a historical recommendation system.

When to use real-time personalization vs. historical recommendation systems

Now that you understand the differences between historical recommendation systems and real-time personalization, along with the technologies behind each, the question remains: when should you choose one over the other? To help make that decision, here are some key questions to ask yourself about your application and user needs.

• Do your users interact with your platform sporadically, like logging in once a day or less frequently? If yes, a historical recommendation system is probably fine.
• Does your platform require instant responses to user actions, like adjusting recommendations based on clicks or recent searches? If yes, consider a real-time personalization system.
• Are you managing vast amounts of historical data and need to analyze user behavior trends over time? If that is the case, implement a historical recommendation system.
• Is your business focused on delivering the freshest data and instant adaptability to maximize user engagement? In that case, real-time personalization is the way to go.
• Is your platform designed for simpler use cases where periodic updates and static recommendations are acceptable? Then a historical recommendation system can be a cost-effective choice.
• Are your users’ behaviors rapidly changing, and does your UI need to adapt quickly to remain competitive? If the answer is yes, adopting real-time personalization is important.

Choosing between real-time personalization or historical recommendation systems.

Use real-time personalization when	Use historical recommendation when
User interactions are frequent and continuous.	User interactions are infrequent and periodic.
The platform needs to respond to rapidly changing behaviors.	User behavior changes slowly over time.
Immediate response times and data freshness are critical.	Longer response times (e.g., daily updates) are acceptable.
Dynamic and scalable architecture is required.	A simpler, cost-effective setup is preferred.
Business priorities focus on gaining competitive advantage.	Cost-efficiency and predictability are the main concerns.

To put it simply, the choice between real-time personalization and historical recommendation systems depends on your business goals, priorities, and user behavior patterns. But the real distinction between them is the real-time aspect and how often the UI is involved in displaying and adapting the interface.

We now know the differences between historical recommendation systems and real-time personalization, and we can focus on how to implement an AI real-time personalization system. We’ll walk through the process, talk about the key steps, and provide a straightforward system design architecture.

 

How AI real-time personalization works

Poorly executed personalization is often more harmful than none at all. To implement a successful AI real-time personalization system, we must delve into its core principles, the steps involved in its process, and the necessary technologies that enable the system to function efficiently.

This section will present a working framework developers can follow to put essential components together for a dynamic and effective personalization solution.

Key principles of real-time personalization

One issue with real-time personalization is where to start. Before exploring the mechanics and technicalities of building such a system, organizations must have a framework to follow.

Algolia’s key principles that help build an effective real-time personalization system include immediacy, balance, transparency, privacy, security, and measurability. These are not theoretical principles, but rather practical guidelines that shape how the system functions and meets user expectations.

Immediacy
The system should be able to adapt and respond to user behavior instantly after just a few meaningful interactions, the system should already begin recommending content or products. These interactions can be anything but should be relevant to chosen events.

Balance
The system needs to find a near-perfect balance in delivering personalized content. While the purpose is to deliver a tailored experience, there should be a fine line between being helpful and being intrusive. Metrics representing user satisfaction towards the product or service can be seen as the consequence of the AI real-time personalization system on UX, and these metrics can prove if the system is working or what to modify to make it more effective.

Transparency
Transparency builds trust. Users should be able to understand why they are seeing a particular recommendation. By explaining the basis of the personalization decisions, whether it’s based on recent activities or any past behaviors, the system becomes more trustworthy. Take a look at this screenshot on an Amazon page displaying a PS5 product:

Amazon explaining the reason behind a recommendation

On the product page, the recommendations feature items customers buy after buying a PS5 under a section titled “What other customers buy after viewing this item?” Even more interesting, when you add an item to the cart, in this case, “Demon’s Souls” you are presented with more interesting recommendations.

The section titled “Recommended for you based on Demon’s Soul - Playstation 5” presents games of the same genre. Shoppers understand why they are suggesting these items. This is transparency well done.

Privacy
Privacy is paramount. Real-time personalization must strictly adhere to privacy regulations, ensuring that only user-consented information is collected and processed. The system should operate with full transparency in terms of data collection practices, and users must have the option to manage their privacy settings.

Security
All data—especially sensitive user information—must be encrypted and stored securely. The system should implement best practices for data protection to safeguard user data from breaches and unauthorized access. This should be done from both ends: starting from the instant data is collected to the moment results are delivered to the frontend.

Measurability
A successful personalization system must be measurable. At the end of the day, personalization is only valuable if it positively impacts user engagement and business metrics. By setting measurable goals like improving click-through rates or increasing average session duration, the system’s effectiveness can be assessed, and strategies can be adjusted as needed.

We now have guiding principles we can use to explore the process of implementing them through specific techniques and technologies.

 

Steps in the process of real-time personalization

Real-time personalization involves several interconnected processes. Each step is critical for capturing user intent, processing data efficiently, and ensuring that the experience is both relevant and immediate.

Data collection
Your system should capture a wide range of user interactions in real-time. We will call these interactions, user intent. This includes actions such as clicks, page views, items added to the cart, hovers, time spent on certain pages, and other engagement metrics.

This is usually achieved using an events tracking mechanism on the frontend, which will send the data to another service that will process this data. That’s where services like Google Analytics, Mixpanel, Segments, or open-source tools like PostHog are helpful. They help collect data and implement real-time connectors to popular data warehouse tools.

Data streaming
Once the data is collected, it needs to be processed continuously, rapidly, and efficiently. With Kafka, we can create a topic that can be accessed by many services such as the real-time recommendation engine, but also other services to store the data in a database to be analyzed more deeply later.

But remember, capturing intent is an iterative process. It begins with simple signals like hovers or clicks and evolves into more complex insights like purchase likelihood or session satisfaction.

Real-time data processing
Real-time data processing is crucial for any AI real-time personalization system. The system must process events on the fly to provide immediate feedback and adapt the user experience.

After data streams into the tools you’ve decided, the processing begins. These tools handle filtering, aggregation, and transformation of data — then the data can be stored in low-latency databases like Apache Cassandra or you can directly access the data from a Kafka topic.

At this stage, machine learning models, such as Contextual Bandit Algorithms, come into play. These algorithms balance exploration and exploitation, enabling quick decision-making and score computations. The models analyze data, generate real-time predictions, and output results in JSON or other consumable formats.

TensorFlow can be used to compute recommendation scores, which are delivered through a real-time API.

To continuously improve the accuracy and relevance of recommendations, user feedback must also be integrated into the real-time processing pipeline. As users interact with personalized content, they may provide explicit feedback in the form of thumbs up/down, ratings, or direct input. This feedback is captured in real-time and used to adjust the score of the affinities computed for the profile used.

Also, evaluating new model versions is important. Implement real-time A/B testing techniques where you can compare different recommendation algorithms or model updates under real-world conditions. This allows you to validate improvements in personalization without disrupting the user experience. Metrics such as mean reciprocal rank (MRR) or discounted cumulative gain (DCG) can give deeper insights into how recommendations rank in real-time contexts.

Real-time API integration
For real-time personalization, the API must operate with minimal latency—often within milliseconds—to ensure a seamless experience for the user. Using load balancers and caching systems can help with that, but you must also decide on how the recommendations are retrieved by the frontend.

One option is to give the user profile data to the frontend, which will decide how to use the scores to order items or fetch relevant ones.

Here is a diagram summarizing the process of a real-time personalization system.

We now have a comprehensive framework with detailed processes and principles to implement a real-time personalization system. However, it’s crucial to recognize that we are working with data.

As mentioned earlier, data integrity and quality are vital characteristics that will determine the effectiveness of your system.

Ensuring quality data
The effectiveness of a real-time personalization system relies on data quality. Prioritizing recent user actions ensures accurate recommendations that reflect current behaviors and intent. As new data comes in, outdated or irrelevant information must be filtered out to maintain accuracy.

Strategies to filter outdated content include:

Event time windows: Consider only recent user events within a specified timeframe to keep recommendations relevant.

Real-time data aggregation: Continuously update aggregated data while removing old entries that no longer represent the user’s behavior.

Session-based filtering: Focus on actions from the current session, discarding older sessions that do not align with current preferences.

Cache expiration: Implement a cache expiration policy to invalidate stale data.

We have outlined the framework and steps for building and maintaining a real-time personalization system, but we have yet to discuss the associated challenges.

 

Challenges of real-time personalization

Many challenges arise when implementing AI real-time personalization. The main ones are related to latency management, data relevance, and security.

Identifying relevant data
The selected data must be relevant for an accurate personalization experience. This process, known as data evaluation, goes beyond simple data collection; it involves understanding user behavior patterns with your product or content.

To effectively evaluate data, you should define, identify, and analyze correlations in collaboration with product, marketing, and design teams, whose actions indicate user intent.

Speed and latency issues
Speed is important to the effectiveness of a real-time personalization system. Users have high expectations nowadays, often leaving a website if it takes more than a few seconds to respond. Studies by Google showed that even small delays, like 400 milliseconds, seriously impact user engagement, search frequency, and overall user satisfaction.

You can achieve low latency and improve speed in your architecture by addressing multiple layers: 



To achieve low latency and improve speed in your architecture, consider the following:

Real-time API: Design APIs to handle a high volume of concurrent requests.

Edge computing and CDN deployment: Process user requests at locations closer to your users to reduce latency.

Caching: Use caching judiciously to prioritize the most recent relevant data. Storing frequently accessed data in in-memory databases like Redis allows for quick retrieval.

Cold start and data sparsity
Real-time personalization can also face a challenge that happens when the system has limited or sparse user data. The issue comes in two forms: the user cold start, where a new user lacks enough historical data for meaningful personalization, and the item cold start, where new products or services have no prior engagement data.

It’s not a problem that can be entirely solved. However, it can be mitigated by using the following strategies:

Popularity-based recommendation: Leveraging regional trends can provide initial recommendations until more personalized data is gathered. 

Hybrid models: A combination of collaborative filtering, popularity-based methods, and content-based methods can allow the system to provide more robust and accurate recommendations, regardless of the lack of data. 

Contextual recommendations: Using contextual information such as the user demographic, location, or time of the day can help enhance the relevance of the recommendations for new users.

The following SQL script, for example, shows how you can solve the cold start problem by recommending products or items bought more recently according to customers in the same location, at this time of the year.

The filter bubble effect

If the lack of initial data makes it complex a personalized experience, there is also a problem that can happen when the personalization system has accumulated too much data on a user, which can dramatically narrow the range of content they are exposed to by continuously reinforcing past preferences.

That quickly leads to a situation where users receive too much-personalized content but miss out on new interests, diversity, and discovery.

François Chalifour, Senior Software Engineer, Algolia has suggested three interesting methods that can be used to mitigate that issue:

Activation mechanism: We can wait to have enough information on the user before applying personalization. This can be enough user events, enough diverse preferences, or after matching the user profile with other user profiles on the system. 

Multi-layered personalization: You can balance the user between multiple personalization layers. 
For example, you can use content appearance which is used by Netflix to expose users to a broader variety of options by varying content placement and visuals for each user.
Content boosting used by Algolia for example, can help prioritize newer content, higher-rated items, or most viewed pages. It will depend on your business needs.

Time decay: Your system can prioritize more recent events while gradually decreasing the impact of older ones on recommendations. The faster the time decay, the more hyper-personalized the experience.

Security and privacy concerns

Security is important in personalization, where data is constantly collected, processed, and stored. A real-time personalization system must ensure the integrity and confidentiality of this data, especially when it involves sensitive user information.

Data collection risks
Data collection for personalization requires strong security protocols to prevent breaches and ensure compliance. Data must be encrypted at rest and in transit with SSL/TLS, and APIs must use multi-layered security, including authentication, access control, and secure session management to prevent tampering.

Compliance with GDPR and CCPA demands transparent data practices, requiring consent, encryption, and strict data usage policies. Privacy-by-design, through data anonymization and minimal data collection, is essential for trust and regulatory alignment.

We now know the processes of implementing an AI personalization system. But what does such a system look like in practice? This is the subject of the following section.

 

Experiment and implementation: AI real-time personalization system architecture and sequential flow

We'll explore the implementation of an AI-powered real-time personalization system for an online clothing store.

The architecture should support scalable growth and real-time performance, enhancing customer satisfaction and evolving with business needs. The following sections will cover core technologies, data flow, and the architectural elements necessary for a seamless real-time user experience.

Technology stack and key components
To build the real-time personalization system, we’ll deploy a set of core, high-performance technologies optimized for scalability and real-time processing:To build the real-time personalization system, we’ll deploy a set of core, high-performance technologies optimized for scalability and real-time processing:

• Frontend (Next.js): Captures user interactions (clicks, views, session data) and displays personalized sections
• Data collection API (Flask): Streams real-time user events to the backend for processing
• Data streaming (Apache Kafka): Manages high-throughput event data, with a dedicated topic for user interactions accessed by the AI recommendation engine
• Data storage (Apache Cassandra): Provides low-latency storage for session and interaction data in a distributed NoSQL environment
• AI personalization engine: Employs tools like Amazon Personalize, TensorFlow, or a custom Contextual Bandit model using ε-greedy for analyzing events and computing user profile scores
• Real-time API: Interfaces between the AI engine and the frontend, delivering profile scores to update the UI dynamically
• Load balancing and caching: Distributes traffic across API servers to prevent overload

Let’s review the system architecture.

System architecture
The architectural layout of this system emphasizes the flow of real-time data from the frontend, through various processing layers, and finally back to the user. The diagram below illustrates how each component interacts to deliver seamless personalized experiences.

AI real-time personalization system design

To implement an AI service for real-time personalization, we will use Contextual Bandits algorithms due to their ability to balance the trade-off between exploration (discovering new recommendations) and exploitation (leveraging known user preferences).

1. Algorithm design

Contextual bandit algorithms derived from the traditional multi-armed bandit problem by incorporating contextual information, enabling more informed and personalized decisions.

Understanding contextual bandits
In the classical multi-armed bandit problem, an agent selects from multiple options called arms to maximize cumulative rewards over time. Each time, the agent faces a trade-off between exploiting known rewarding options and exploring less certain ones to discover potentially higher rewards. Contextual bandits improve the framework by evaluating additional information or covariates called context available at each decision point. This context can include the time of the day, the date, user demographics, even old decisions made in the past, and other relevant features, allowing the agent to tailor its actions based on the current circumstances carefully. 

In the contextual bandit problem, each iteration unfolds as follows:

1. Context presentation: The environment provides a context, represented by a fixed-dimensional vector of covariates, X.

2. Reward distribution: Based on this context, each action (or arm) has a reward associated with it, represented by a stochastic vector of rewards r. However, only the reward of the selected action is observed, creating a partial feedback scenario.

3. Action selection: The agent, using the available context, chooses one arm among the set of possible actions.

4. Reward feedback: The reward for the chosen arm is revealed, while the rewards for the unchosen arms remain unknown.

The challenge lies in balancing the need to explore untried actions (arms) to gain more information about their rewards and the desire to exploit known actions to maximize cumulative rewards — a familiar exploration-exploitation dilemma.

And this is where the choice of an algorithm is crucial. Here are the most common ones and their variations.

ε-greedy
The ε-greedy strategy dynamically balances between exploiting the best-known solutions and exploring new options. This algorithm compares the average outcomes, and target values, y for each choice or condition to identify the most rewarding options.  

This can be interpreted with the following formula: . where n_dx represents the number of observations for an option d in a given context x, and y_idx is the outcome for each observation. In practice, ε-greedy presents the best-rated options most of the time (with a probability of 1 - ε), but occasionally (with a probability of ε) presents a random option to discover new user preferences. A variation of this algorithm is the epsilon-decreasing strategy, where ε decays over time because with the system learning more about the user, the need for exploration becomes less. Another variation is the epsilon-first strategy, where the algorithm will act randomly for a fixed period, to learn more about the best options, and then exploit these options.

Upper confidence bound
Epsilon greedy explores options randomly, which can be inefficient in certain cases and may miss valuable options, especially in high-dimensional contexts. The UCB strategy prioritizes options with high average outcomes while factoring in the certainty of these estimates. It calculates the next condition by combining both the outcome estimate and the confidence in that estimate, the highest likely value of the upper confidence bound. So we have:  where c is a constant adjusting exploration focus, N is the total number of trials, and is the count for option d in context x. The c constant represents an adjustable level of confidence size, which determines exploration aggressiveness.

This approach is common in systems like targeted advertising, where an ad is shown more often if it has both a high click-through rate and few impressions, allowing for rapid learning of effective ads. UCB’s advantage is its balance of exploration and exploitation, but in highly various contexts, it may require refined models like LinUCB or Bayesian UCB to handle complexities.

Thompson Sampling
Thompson Sampling uses a Bayesian approach to solve the exploration-exploitation dilemma, selecting options based on the probability that they are the best choice given the data observed so far. It models an expected outcome y for each option under conditions such as D and context X, using a set of learnable parameters to predict the outcomes. The model can be defined as follows: f(D,X;0).

Thompson Sampling uses Bayesian inference to sample from the posterior distribution of each option's reward probability, selecting the option with the highest sampled reward. Each sample represents the potential reward under current uncertainty, and the option with the highest sampled value is chosen.

2. Seed data and the cold start problem

To address the cold start problem, we rely on a strategy that prioritizes well-rated items and popular categories, augmented by local popularity.

Well-rated items: For users with no prior interactions, we select items based on their ratings. Each item has a rating score Ri on a scale (1 to 5), we rank items by Ri and display those with the highest scores.  

Regional popularity: Besides ratings, we can consider items popular in the user’s region, using recent purchases or view data. For each item i, Pi represents the count of views or purchases in the user's region over a specific period T. The items with the highest Pi values are selected, effectively highlighting trending items. This popularity-based selection is defined as:

3. Training and exploration/exploitation trade-off

The system implements an ε-greedy algorithm to manage the exploration/exploitation trade-off. The Vawpal Wabbit packages allow us to initialize an agent based on this algorithm, with interesting parameters such as the exploration parameter and the weight decay.

4. Real-time inference and API integration

The inference process would start with the API fetching the context data (location, device, time) and passing it into the model. The model computes the user profile with the most relevant recommendations based on the context and past user behavior and returns a ranked list of personalized items.

The real-time API would integrate this inference process seamlessly into the user flow. In our case, we will expose the user profile to the frontend so we will have control over the sections shown on the frontend.

Let’s now discuss more about the data flow, to understand how the components interact with each.

Sequential flow of the system

Let’s introduce a sequential diagram to demonstrate the data flow between components when a user acts. This breakdown shows how each system component works together in a step-by-step fashion.

AI personalization system sequential flow

Here is a description of this sequential flow:

• User interaction (click/hover): The user interacts with the frontend interface by clicking or hovering over a product or content.
• Frontend sends event: The frontend captures the interaction and sends it to the API for processing.
• API pushes event to Kafka queue: The API forwards the interaction event to a Kafka queue for asynchronous processing, enabling high throughput.
• Kafka streams event for processing: Kafka queues the event and streams it to the data storage and AI personalization engine simultaneously.
• Data storage (Cassandra): Cassandra stores the event data for future reference and ensures low-latency access for historical analysis.
• AI personalization engine: The AI personalization engine processes the event data in real-time to generate personalized recommendations based on user behavior.
• AI personalization engine sends recommendation scores: The AI personalization engine computes and forwards personalized scores to the real-time API.
• Real-time API delivers recommendations: The API retrieves the scores and updates the frontend with personalized content.
• Frontend displays personalized content: The user interface dynamically displays the personalized recommendations, completing the loop.

And here is how we can build an AI real-time personalization system. The keywords here are low latency and high availability, and this architecture is well-designed to handle those things.

 

Experiment and implementation: Personalizing the experience for customers on an ecommerce website using Next.js and Python

In this experiment, we implement the proposed architecture to achieve real-time personalization for an ecommerce website. The primary goals are to establish a robust data collection system, design an efficient real-time data pipeline that continuously feeds the recommendation engine, and make sure user profiles are available to the frontend for usage.

Home page showing two distinct recommendations sections

Nothing fancy here: we have two pages, one to display a list of products and one to display a page regarding a product.

We are using the Amazon Products Dataset 2023, comprising over 1.3 million products, to test scalability and accuracy in handling large datasets for real-time recommendations. The dataset provides a diverse range of items to simulate varied customer interactions and preferences within the recommendation system.

When populating the database with the data from the dataset, make sure to add a field called rating, the mean rating of rated items in that category, excluding unrated items. By default, categories are ranked by rating​, and the top categories are displayed on the home page before the recent products, to address the cold start issue.

With this in mind, let’s discuss the data collection.

Collecting the data

Collecting data is the first part of establishing the application. For this example, we are collecting four types of events: product views, category views, adding items to a cart, and cart checkout.

Each request for an event sent to the data collection API must contain an ID, that will help us identify the user when analyzing the events and creating user profiles.

For our example, we will use a client-generated UUID stored on the localStorage of the client machine, with a lifespan of 48 hours, which we decided to declare as the average session time.

Here is a piece of code used to send event requests from the frontend to an API endpoint called track.

We have a function called getSessionId() , which helps us retrieve a session ID that will be attached to the request.

In the case of tracking multiple events and to avoid sending dozens of requests to the API, it’s conceivable to send events in batches. An arbitrary number can be sent when 10 events are registered. This is useful if you are planning to collect more complex data such as hover viewing time or other data. You can use tools like Redux, saving events in the store. When you have the number of events you can send in a batch and trigger a request.

The events tracking requests are made to the endpoint which will take these events and process them in a Kafka topic which we will call user_interactions.

The events are ingested in Kafka, and we just need to have a service subscribed to these topics, first a service to add the data to a data store like Cassandra for big analysis. If you are using a tool instead of your custom implementation, you might want to look at the connectors available. For example, using Mixpanel, you can implement a connector that streams your Mixpanel data directly into a Kafka topic.

The data is available in Kafka, and we can access it. Let’s discuss the crucial part of this application: the AI recommendation engine.

The AI recommendation engine
The real-time recommendation engine implements an event-driven approach to personalized recommendations, leveraging a Contextual Bandit model with Vowpal Wabbit. The workflow contains event streaming, contextual model processing, recommendation storage, and ongoing feedback adaptation.

Event Streaming and Deduplication
The system continuously listens for user interaction events on the user_interactions Kafka topic, where each event includes critical session-specific data such as session_id and timestamp. Upon receiving an event, the system checks the latest processed timestamp for the session in Redis. If the event’s timestamp is older than or matches the stored timestamp, it is discarded to prevent duplicate processing. This ensures that the engine operates on fresh, unique events, maintaining an up-to-date context for each user.

Using the process_events_with_cb function in the previous code, we read events from Kafka and process them. If there is a feedback event, we automatically capture this event and process the feedback.

To discuss more about the retraining and processing and events, let’s focus more on the RecommendationEngine class, the core of our real-time recommendation engine.

Real-time RecommendationEngine class
The RecommendationEngine class employs Vowpal Wabbit’s (VW) Contextual Bandit model to handle real-time recommendations based on user interactions. Vowpal Wabbit’s workspace will be initialized with --cb_explore_adf for contextual bandit functionality and --epsilon 0.2 for epsilon-greedy exploration, where epsilon controls the balance between exploration and exploitation. With epsilon set to 0.2, the system explores alternative recommendations 20% of the time, making it suitable for relatively stable user behavior without excessive variability.

For each event in the batch, the model predicts a probability distribution over actions, which is weighted by a decay factor that prioritizes recent interactions. The score for each action (product) is calculated as:

Where:

• Reward is derived from the event type (e.g., purchase events receive higher rewards than simple views).
• Probability (prob) is the model’s predicted likelihood of user engagement with the action.
• Weight is a time-decay factor (0.9) that gives more significance to recent interactions.
• Profile Relevance is additional relevance from the user’s profile score for the action, which ensures items aligned with the user’s history get higher scores.

Methods and processes of the RecommendationEngine class

• Context retrieval: The get_context function gathers contextual features from each user event, including session ID, and time of day, and optionally integrates high-affinity products from the user profile, forming a comprehensive context vector for VW’s input.
  
• Action selection: The get_possible_actions function retrieves a set of products for recommendation, balancing recency, popularity, and profile affinity. It first gathers category-specific products, followed by high-rated popular products, ensuring that actions align with both general and individual user preferences. Actions are then shuffled and prioritized according to the user profile. In a more concrete project, instead of having to query the db directly, you can have a set of precomputed preferences of the user’s. This function can then work with this set, created by a traditional recommending system, to create better profiles.
  
• Event processing: The process_event_batch function computes recommendation scores by processing each event in a batch. Context is extracted for each event, actions are fetched, and rewards are calculated based on event type, representing user response strength to specific actions. Each action is evaluated with VW’s predict function, and probability-weighted scores are computed.
  Scores are weighted by applying a time-based decay factor to prioritize recent events. The method applies weight decay through a multiplicative factor for each successive event in reverse order. Each action’s probability score is adjusted based on user profile affinities, and a normalized final score is calculated. These scores, sorted in descending order, yield the top recommendations for the user.
  
• Feedback processing and model update: The process_feedback method integrates feedback into the model by adjusting the reward signal. This adjustment accounts for both feedback type and profile affinities. Positive feedback on high-affinity products raises the reward value, reinforcing specific affinities. The method formats the VW example and calls learn to update VW’s internal model, enabling continuous tuning based on user feedback.
  
• Redis caching: Final recommendation scores and user profiles are cached in Redis, allowing fast retrieval by the frontend, which can immediately display updated recommendations based on real-time user actions and preferences. Naturally, these profiles should be saved in a database like Cassandra, for processing later. For transparency, it is also important to have these profiles saved, so at any time of a user session, we can explain why these categories and products were recommended to the user. Here is the shape of the user profile:
  
  To enable the frontend to know when to fetch a new profile, one option is to set a default expiration time for each computed profile. After this interval elapses, the frontend automatically requests a refreshed profile. While straightforward, this method may not scale well for fast real-time recommendation needs, such as during high-traffic events like Black Friday, where rapid, relevant recommendations are important.
  
  In scenarios demanding more regular updates, we can implement a WebSocket or Server-Sent Events (SSE) service. With this setup, when a new profile is computed, the backend sends a notification to the frontend through these channels. The notification prompts the frontend to immediately fetch the latest profile, guaranteeing recommendations remain timely and contextually relevant.
  
  Let’s talk about displaying the recommendations, which is the most important part because it covers personalization.

Display the recommendations
To effectively integrate and display real-time recommendations in a frontend application, a comprehensive strategy is necessary to ensure that the UI reflects personalization without overloading the backend.

User experience and contextual personalization
UI is the central component of personalization. The design and placement of recommendations should feel natural and relevant, enhancing the user's experience. In traditional systems, recommendations are often precomputed and stored in a cache, waiting for the user’s next session. Platforms like X, formerly Twitter, have historically used this approach, preparing recommendations and posts in advance. When the user returns, these precomputed elements are displayed without hitting the database.

For real-time AI personalization, our system needs a responsive setup to avoid putting excessive pressure on the backend. This is where context and interface flexibility come in. The frontend should dynamically select recommendations based on available score data from the backend, making the UI come alive by adapting instantly.

Making the interface interactive and engaging
We should start recommendations slowly, and in a crucial part of the application. A good beginning should be to replace the top categories, with a similar component representing a list of articles the user might like, based on recent history. For example, we can place a section called, “Items You May Love Based on Your Recent Interactions.”

Showing personalized section, “Items You May Love Based on Your Recent Interactions”

The name of the section should depend on and adapt to the type of affinities you are using. For example, if you are displaying trending items the user might like, the text should be “Trending Items You May Love.”

Ensuring user score accessibility across components
The user profile should be globally accessible to enable updates in various parts of the interface. Here, using React Context API or Redux is ideal for real-time score updates without disrupting the flow. That setup allows efficient updates on user profile changes, keeping components in sync with the latest recommendations.

Using Next.js, here is an example of how you can achieve it.

We can use this Context in the Layout of your application.

The scores are now accessible in the whole application, we can extend the feature of our personalization system further.

One effective strategy involves designing recommendation strategies around bundled discounts. By introducing a structure in the user score that incorporates products frequently bought together or that offer discounts when purchased in combination, recommendations become more engaging and potentially more valuable to the user.

For instance, if the user score includes items that offer a discount when bought together, the UI can display “bundle deals” that not only enhance personalization but also add value through savings. If a user completes a purchase based on a bundled discount, this interaction serves as strong positive feedback, reinforcing the recommendation model and contributing to future optimizations.

Here’s a potential structure for the user score that accommodates these additional dimensions:

In this structure:

• score_data: Holds standard recommendation scores based on user interactions.
• bundled_discount_suggestions: Lists products that offer discounts when purchased together. Each bundle includes a primary product and associated bundle items, with a calculated score for each bundle deal.
• trending_recommendations: Highlights trending items that align with the user’s profile.

When this structure is passed to the components, it can be used in specific ones for displaying trending recommendations or bundle suggestions.

Implementing feedback loops for continuous learning
Incorporating user feedback refines recommendation quality. In our case, positive feedback (e.g., adding a recommended item to the cart) can trigger an event request to the data collection API.

Conversely, viewing but not engaging with a recommendation can be treated as neutral or negative feedback.

After the models are set up and optimized for real-time performance, it’s essential to implement the right monitoring tools to continuously analyze system behavior and model effectiveness. Monitoring user feedback, updating recommendation models on the fly, and refining your data pipelines will ensure long-term success.

With these principles, your business is ready to deliver a seamless and personalized experience that increases user satisfaction, drives conversions, and fosters long-term customer loyalty.

If your organization is looking for an efficient way to achieve growth through AI-driven personalization, Algolia can fast-track the implementation process. With built-in machine learning and personalization tools, Algolia’s solutions allow you to achieve real-time customization without the heavy investment of building your engine from scratch. Algolia’s customizable, low-latency platform can help you tailor recommendations based on user behaviors and preferences while saving development time and resources.

 

Conclusion

Let’s summarize the key concepts of implementing AI-driven real-time personalization for modern businesses.

• Real-time personalization hinges on employing the right tools, including Contextual Bandit algorithms, scalable data pipelines, and low-latency APIs, to instantly ensure users get tailored, relevant content.
• Successful implementations start with high-quality data, ensuring that your personalization system accurately reflects user preferences and behaviors in real-time. Data freshness, low-latency response times, and scalability are important components of a high-performing system.
• Consider the challenges we discussed, such as handling the cold start problem, managing bias, and ensuring continuous learning in your AI models. Applying strategies like contextual recommendations and real-time feedback loops can ensure the system evolves with user interactions.