Real-Time Recommendation Systems

After spending a year building a real-time recommendation engine at scale, I’ve compiled an all-encompassing guide that covers everything you need to know: Introduction: - Leveraging Kafka, Spark Streaming, and Lambda APIs to power consumer personalization at Nike has been a game-changer in enhancing the shopping experience. Step-by-Step Process: 1. **Data Ingestion**: Utilize Kafka to stream user interactions and product data in real-time, ensuring a continuous flow of information. 2. **Stream Processing**: Implement Spark Streaming to process the incoming data, performing real-time analytics and generating immediate insights on consumer behavior. 3. **Recommendation Algorithm**: Develop a collaborative filtering algorithm using Lambda APIs to deliver personalized product recommendations based on user preferences and previous purchases. 4. **Feedback Loop**: Establish a feedback mechanism to capture real-time user responses, refining the recommendations and improving accuracy over time. Common Pitfalls: - Overlooking data quality can lead to inaccurate recommendations; ensure rigorous validation and cleansing steps are in place. - Ignoring latency issues can degrade user experience; optimize your pipeline to minimize response time for real-time interactions. Pro Tips: - Monitor your Kafka topics closely to detect anomalies early. - Use feature engineering to enhance recommendation algorithms by incorporating additional user attributes. FAQs: - How does Kafka handle high throughput? Kafka’s partitioning and replication features enable it to efficiently manage large volumes of messages. - Can Spark Streaming integrate with other data sources? Yes, Spark Streaming seamlessly integrates with various sources and sinks, allowing flexibility in your data pipeline. Whether you’re a data engineer keen on building robust systems or a product manager looking to leverage personalization, this guide is designed to take you from ideation to implementation. Have questions or want to add your own tips? Drop them below! 📬

This concept is the reason you can track your Uber ride in real time, detect credit card fraud within milliseconds, and get instant stock price updates.  At the heart of these modern distributed systems is stream processing—a framework built to handle continuous flows of data and process it as it arrives.     Stream processing is a method for analyzing and acting on real-time data streams. Instead of waiting for data to be stored in batches, it processes data as soon as it’s generated making distributed systems faster, more adaptive, and responsive.  Think of it as running analytics on data in motion rather than data at rest.  ► How Does It Work?  Imagine you’re building a system to detect unusual traffic spikes for a ride-sharing app:  1. Ingest Data: Events like user logins, driver locations, and ride requests continuously flow in.   2. Process Events: Real-time rules (e.g., surge pricing triggers) analyze incoming data.   3. React: Notifications or updates are sent instantly—before the data ever lands in storage.  Example Tools:   - Kafka Streams for distributed data pipelines.   - Apache Flink for stateful computations like aggregations or pattern detection.   - Google Cloud Dataflow for real-time streaming analytics on the cloud.  ► Key Applications of Stream Processing  - Fraud Detection: Credit card transactions flagged in milliseconds based on suspicious patterns.   - IoT Monitoring: Sensor data processed continuously for alerts on machinery failures.   - Real-Time Recommendations: E-commerce suggestions based on live customer actions.   - Financial Analytics: Algorithmic trading decisions based on real-time market conditions.   - Log Monitoring: IT systems detecting anomalies and failures as logs stream in.  ► Stream vs. Batch Processing: Why Choose Stream?   - Batch Processing: Processes data in chunks—useful for reporting and historical analysis.   - Stream Processing: Processes data continuously—critical for real-time actions and time-sensitive decisions.  Example:   - Batch: Generating monthly sales reports.   - Stream: Detecting fraud within seconds during an online payment.  ► The Tradeoffs of Real-Time Processing   - Consistency vs. Availability: Real-time systems often prioritize availability and low latency over strict consistency (CAP theorem).  - State Management Challenges: Systems like Flink offer tools for stateful processing, ensuring accurate results despite failures or delays.  - Scaling Complexity: Distributed systems must handle varying loads without sacrificing speed, requiring robust partitioning strategies.  As systems become more interconnected and data-driven, you can no longer afford to wait for insights. Stream processing powers everything from self-driving cars to predictive maintenance turning raw data into action in milliseconds.  It’s all about making smarter decisions in real-time.

In our journey of building personalized recommendations, we often debate when models should run in real-time vs. batch processing. It completely depends on use case, scalability, and latency that is acceptable. Let me try to simplify it so that you can explain it better to your management - 1) Real-Time Models – When Instant Personalization is Key. This flow is used when recommendations must be generated instantly based on a user’s current actions. Example Use Cases: "You May Also Like" – A user clicks on a product, and recommendations are generated dynamically. Personalized Home Page – When a user logs in, their recommendations are fetched in real time. Dynamic Offers – Based on recent user behavior, a discount or coupon is displayed immediately. This is how it can be implemented if using Amazon Web Services (AWS): 🔹 User Action → A user visits a webpage or clicks on a product. 🔹 API Gateway + Lambda → Triggers an API call to fetch recommendations. 🔹 Model Prediction (SageMaker Endpoint) → If no cached results exist, the model generates new recommendations. 🔹 DynamoDB / Redis Cache → First checks for recent recommendations to reduce latency. 🔹 Response to Frontend → Results are returned and displayed instantly. 2) Batch Processing – Precomputed Recommendations This approach is used when personalization can be precomputed, reducing the need for real-time execution. Example Use Cases: "Your Favorites" (Rule-Based Personalization) – If a user buys from X retailers frequently, precompute recommendations daily. Periodic Email / Push Notifications – Personalized product suggestions for email marketing campaigns. Homepage Personalization (Static User Preferences) – Daily updates to improve page load speed. This is how it can be implemented: 🔹 Daily / Weekly Training Jobs (Glue, SageMaker, EMR) → or you can use dedicated EC2 & Jenkins to process large amounts of data and update recommendations. 🔹 Updated Recommendations Stored (DynamoDB, Redis) 🔹 Precomputed Recommendations Served via API / CloudFront So, if recommendation changes dynamically basis user session, use real time. For predictable updates use batch. Infact, one can use hybrid approach also - Cache precomputed results and fall back on real-time inference when needed. #recommendation #n=1personalisation #datascience #data

We’re all used to getting product recommendations when we hit a site. Problem is that the recommendations are often driven more by ad dollars than our own preferences. What’s more, they are usually pre-computed in batches, so don’t reflect the latest state of product availability. This is a challenge the data science team at Delivery Hero has tackled. By building the company’s new Item Replacement Tool on MongoDB Atlas Vector Search, their systems can generate personalized product recommendations in real-time for fast-moving, perishable items. What’s even more impressive, they do this at a global scale, with results returned in less than a second. With MongoDB Atlas, Delivery Hero can store, index, and query vector embeddings right alongside its product and customer data — all fully synchronized. It's only with this integrated, platform approach that they can offer hyper-relevant personalized recommendations, boosting revenues and improving customer satisfaction while reducing costs and complexity. Read more in our case study: https://lnkd.in/gN7-DhwQ

Improving recommendations isn’t just about better models — it’s about creating more meaningful user experiences. In our latest work at CVS Health 🏥, Madhumita Jadhav and I enhanced the "You May Also Like" (YMAL) system by combining the contextual power of LLMs with the efficiency of Word2Vec. The result: smarter, faster, and more relevant suggestions. 🔗 Read the full details on the CVS Health Tech Blog - https://lnkd.in/djiwVvbx Inside the post: ✨ A look at our hybrid architecture ⚙️ Practical lessons from real-world deployment 💡 Why LLMs and traditional embeddings work better together Grateful to collaborate with such a sharp and supportive team 🙌 Madhumita Jadhav, Sarah Boukhris-Escandon, PhD, PE, Jyothsna Santosh, Sowmya Vasuki Jallepalli, Ryan Berns and more! Always curious to hear how others are applying LLMs in production — feel free to share your thoughts! #AI #LLM #RecommenderSystems #MachineLearning #CVSHealthTech #TechInnovation #NLP

Forget classical Deep Learning Recommendation Models (DLRMs). Generative Recommenders are changing the game: Generative Recommenders (GRs) are a novel paradigm that formulate ranking and retrieval as sequential transduction tasks, allowing them to be trained generatively. The Hierarchical Sequential Transduction Unit (HSTU) encoder enables generating user interactions autoregressively. HSTU's pointwise aggregated attention and relative attention biases are key architectural advancements. It is 5-15x faster than SOTA transformers on long sequences. More importantly, GRs exhibit superior scaling properties. Their quality scales as a power law of training compute across three orders of magnitude. In contrast, DLRMs saturate in performance at large scales. The M-FALCON algorithm further improves GR inference latency and throughput, enabling 285x more complex models with similar inference budget. Is this the ChatGPT moment for recommender systems? 🤔 GRs pave the way for foundational models in recommendations. The unified feature space allows models to train and generalize across domains. The generative formulation opens new possibilities for end-to-end recommendations. User actions are an under-explored yet potent data modality. GRs show that for recommendations, actions indeed speak louder than words! #RecommenderSystems #genai #GenerativeAI

Recommender systems have always been a topic of great interest to me as they are such widely used. The main goal of any recommender system, whether at Netflix, LinkedIn, TikTok, or Instagram, is to help users discover content they will enjoy and maximize long-term satisfaction. Thus it becomes a complex challenge due to the uniqueness of each user, the variety of their interests, and the changing contexts. 🌟 Understanding this goal, I always thought about the idea of using Deep Learning techniques to solve these challenges and that's when I came across an interesting Netflix case study by Harald Steck, Linas Baltrunas, Ehtsham Elahi, Yves Raimond, and Justin Basilico. The key learnings from the case study were: 𝟭. 𝗕𝗮𝗴-𝗼𝗳-𝗜𝘁𝗲𝗺𝘀 𝗠𝗼𝗱𝗲𝗹𝘀: Initially, Netflix used these models for their simplicity and effectiveness in capturing long-term interests but found that they ignored temporal sequences. To address this, they incorporated time and context, resulting in significant improvements in ranking and recommendations. 𝟮. 𝗦𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝘀: These models excel in identifying current user interests by analyzing the order of interactions (temporal sequence) though they might not be able to capture the entire spectrum of long-term interests. 𝟯. 𝗔𝘂𝘁𝗼𝗲𝗻𝗰𝗼𝗱𝗲𝗿𝘀 𝗮𝗻𝗱 𝗛𝗲𝘁𝗲𝗿𝗼𝗴𝗲𝗻𝗲𝗼𝘂𝘀 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: These were introduced to distill user preferences more efficiently and to enhance personalization, but they brought complexities like overfitting and data management challenges. 𝟰. 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀: These marked a significant advancement in recommendation accuracy and personalization but introduced challenges in training, data management, and metric alignment. Other challenges included scaling the system, integrating deep learning toolboxes, and managing vast user base. The study also emphasizes that at Netflix, a variety of algorithms are employed for specific recommendation tasks, such as selecting the homepage's top video or helping users discover new content, highlighting that the optimal method varies based on the task and data at hand. In conclusion, while deep learning models offer significant enhancements, they also introduce complexities in training and data management, essential considerations for long-term user satisfaction. Link to the paper: https://lnkd.in/gvETh-An Lemme know if I missed something 😊 #machinelearning #recommendersystems #deeplearning #womenintech #machinelearningengineer #opentowork #recuiters #networking