Utilizing Data To Enhance Product Recommendations

Exciting breakthrough in e-commerce recommendation systems! Walmart Global Tech researchers have developed a novel Triple Modality Fusion (TMF) framework that revolutionizes how we make product recommendations. >> Key Innovation The framework ingeniously combines three distinct data types: - Visual data to capture product aesthetics and context - Textual information for detailed product features - Graph data to understand complex user-item relationships >> Technical Architecture The system leverages a Large Language Model (Llama2-7B) as its backbone and introduces several sophisticated components: Modality Fusion Module - All-Modality Self-Attention (AMSA) for unified representation - Cross-Modality Attention (CMA) mechanism for deep feature integration - Custom FFN adapters to align different modality embeddings Advanced Training Strategy - Curriculum learning approach with three complexity levels - Parameter-Efficient Fine-Tuning using LoRA - Special token system for behavior and item representation >> Real-World Impact The results are remarkable: - 38.25% improvement in Electronics recommendations - 43.09% boost in Sports category accuracy - Significantly higher human evaluation scores compared to traditional methods Currently deployed in Walmart's production environment, this research demonstrates how combining multiple data modalities with advanced LLM architectures can dramatically improve recommendation accuracy and user satisfaction.

How Netflix Turned Data into a $1 Billion Product: Lessons for Product Managers 🚀 Imagine a product feature so impactful that it directly drives 80% of user engagement. That’s Netflix’s Recommendation System, which contributes a staggering $1 billion annually to the company's revenue. Let’s dive into how Netflix built this game-changing feature and what product managers can learn from it. Problem Netflix Faced in 2007: Users were overwhelmed by the sheer volume of choices, leading to decision fatigue. As a result: 75% of users were abandoning the app without selecting anything. Poor content discovery resulted in lower retention. Netflix needed a way to make finding content seamless, engaging, and personalized. The Solution: Netflix introduced a data-driven recommendation system, leveraging: 1️⃣ Behavioral Data: What users watched, skipped, or paused. 2️⃣ Collaborative Filtering: Recommendations based on similar users’ preferences. 3️⃣ Deep Learning Algorithms: To refine content suggestions continuously. Results Achieved: Increased retention rates by 10%, which translated into millions of dollars in revenue. Reduced content discovery time by 60%, improving user satisfaction. Recommendations now drive 80% of total views on Netflix. Key Lessons for Product Managers: 🧠 1. User-Centric Design is Everything Netflix doesn’t just recommend “popular” shows. It curates suggestions based on what users are likely to love, ensuring every interaction feels personal. 💡 Takeaway: Build features that solve your users' specific problems. 📊 2. Leverage Data as Your Superpower Netflix’s algorithm is powered by billions of data points. They don’t guess what users want—they know. 💡 Takeaway: Make data your north star. Let insights, not intuition, drive decisions. ⏳ 3. Continuously Iterate The Netflix recommendation system wasn’t perfect from day one. It evolved through constant experimentation and feedback. 💡 Takeaway: Embrace the “launch, learn, and improve” mindset.

Knowledge Graphs (KGs) have long been the unsung heroes behind technologies like search engines and recommendation systems. They store structured relationships between entities, helping us connect the dots in vast amounts of data. But with the rise of LLMs, KGs are evolving from static repositories into dynamic engines that enhance reasoning and contextual understanding. This transformation is gaining significant traction in the research community. Many studies are exploring how integrating KGs with LLMs can unlock new possibilities that neither could achieve alone. Here are a couple of notable examples: • 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐃𝐞𝐞𝐩𝐞𝐫 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Researchers introduced a framework called 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐆𝐫𝐚𝐩𝐡 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐀𝐠𝐞𝐧𝐭 (𝐊𝐆𝐋𝐀). By integrating knowledge graphs into language agents, KGLA significantly improved the relevance of recommendations. It does this by understanding the relationships between different entities in the knowledge graph, which allows it to capture subtle user preferences that traditional models might miss. For example, if a user has shown interest in Italian cooking recipes, the KGLA can navigate the knowledge graph to find connections between Italian cuisine, regional ingredients, famous chefs, and cooking techniques. It then uses this information to recommend content that aligns closely with the user’s deeper interests, such as recipes from a specific region in Italy or cooking classes by renowned Italian chefs. This leads to more personalized and meaningful suggestions, enhancing user engagement and satisfaction. (See here: https://lnkd.in/e96EtwKA) • 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠: Another study introduced the 𝐊𝐆-𝐈𝐂𝐋 𝐦𝐨𝐝𝐞𝐥, which enhances real-time reasoning in language models by leveraging knowledge graphs. The model creates “prompt graphs” centered around user queries, providing context by mapping relationships between entities related to the query. Imagine a customer support scenario where a user asks about “troubleshooting connectivity issues on my device.” The KG-ICL model uses the knowledge graph to understand that “connectivity issues” could involve Wi-Fi, Bluetooth, or cellular data, and “device” could refer to various models of phones or tablets. By accessing related information in the knowledge graph, the model can ask clarifying questions or provide precise solutions tailored to the specific device and issue. This results in more accurate and relevant responses in real time, improving the customer experience. (See here: https://lnkd.in/ethKNm92) By combining structured knowledge with advanced language understanding, we’re moving toward AI systems that can reason in a more sophesticated way and handle complex, dynamic tasks across various domains. How do you think the combination of KGs and LLMs is going to influence your business?

As an analyst, I was intrigued to read an article about Instacart's innovative "Ask Instacart" feature integrating chatbots and chatgpt, allowing customers to create and refine shopping lists by asking questions like, 'What is a healthy lunch option for my kids?' Ask Instacart then provides potential options based on user's past buying habits and provides recipes and a shopping list once users have selected the option they want to try! This tool not only provides a personalized shopping experience but also offers a gold mine of customer insights that can inform various aspects of a business strategy. Here's what I inferred as an analyst : 1️⃣ Customer Preferences Uncovered: By analyzing the questions and options selected, we can understand what products, recipes, and meal ideas resonate with different customer segments, enabling better product assortment and personalized marketing. 2️⃣ Personalization Opportunities: The tool leverages past buying habits to make recommendations, presenting opportunities to tailor the shopping experience based on individual preferences. 3️⃣ Trend Identification: Tracking the types of questions and preferences expressed through the tool can help identify emerging trends in areas like healthy eating, dietary restrictions, or cuisine preferences, allowing businesses to stay ahead of the curve. 4️⃣ Shopping List Insights: Analyzing the generated shopping lists can reveal common item combinations, complementary products, and opportunities for bundle deals or cross-selling recommendations. 5️⃣ Recipe and Meal Planning: The tool's integration with recipes and meal planning provides valuable insights into customers' cooking habits, preferred ingredients, and meal types, informing content creation and potential partnerships. The "Ask Instacart" tool is a prime example of how innovative technologies can not only enhance the customer experience but also generate valuable data-driven insights that can drive strategic business decisions. A great way to extract meaningful insights from such data sources and translate them into actionable strategies that create value for customers and businesses alike. Article to refer : https://lnkd.in/gAW4A2db #DataAnalytics #CustomerInsights #Innovation #ECommerce #GroceryRetail

🚀 How do you ensure your customers see what they want to see — not just what you want to show? With AI and ML becoming core to ecommerce (both B2B and B2C), product discovery is getting a lot of attention. And rightly so. But here's the truth: most recommendation engines fail not because the models are bad, but because the first two steps were never right. Let me explain. Many product managers (especially in fast-paced orgs) jump into building rec engines with a "let's plug in collaborative filtering and see how it goes" mindset. But without clearly defining what type of recommendation makes sense for your use case — and how it ladders up to a business metric — you're setting yourself up for rework. Here's how I approach it when working with teams: Step 1: Business Understanding: Start with the why before touching the how. ◾ What are you recommending? Products? Content? Users? Services? ◾What does success look like? Higher CTR? More revenue? Better retention? ◾Where will it show up? Homepage, PDP, cart, email, app banner? ◾What constraints exist? Does it need to be real-time? Can it be batched overnight? Without alignment on this, even the most advanced ML model will fall flat. Step 2: Choose the Right Recommendation Type: Now comes the how — but it should be tailored to your product + user journey. ◾Content-based filtering: “You liked this, so you’ll like these similar items.” ◾Collaborative filtering: “Users like you also bought this.” ◾Hybrid models: The best of both worlds — widely used in ecommerce and streaming. ◾Knowledge-based systems: Rule-driven, useful when personalization is constrained (e.g., insurance, banking). Let me make this concrete with a simple example: Imagine you’re building a recommendation module for a first-time visitor on your site who hasn’t logged in. If you apply collaborative filtering, it’ll fail — there’s no past data to compare. But if you use content-based filtering on the item they’re browsing and pair it with trending items, you instantly make the experience better. It’s not about which model is smarter. It’s about which makes sense for the scenario. Let’s be honest — your recommendation engine’s success doesn’t start with machine learning. It starts with product thinking. #AI #ProductManagement #Ecommerce #Personalization #RecommendationEngine #ProductStrategy I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal Vignesh Kumar

🔎 Seeking ways to level up your recommender systems? Look no further! LinkedIn’s recent industrial recommender systems paper provides practical insights spanning model architectures, training procedures and deployment hurdles - contributing to significant metric gains across their products. 🔮 Let’s dive into the highlights: 1. Residual DCN Layer: The proposed Residual DCN layer enhances the Deep & Cross Network v2 (DCNv2) architecture by adding attention and residual connections, improving the model’s ability to capture complex feature interactions 💪 2. Isotonic Calibration Layer: A customized isotonic regression layer that can be trained jointly with the deep neural network to perform calibration helps align the predicted probabilities with real-world distributions 🎯 3. Production-Ready Exploit/Explore Methods: Deep learning-based exploit/explore methods are customized for production use, balancing exploiting historical user data to maximize immediate performance versus exploring new items to aid longer-term performance 🚀 4. Model Convergence Improvements: Various techniques including longer learning rate warm-up, batch normalization and increased training steps paired with higher learning rates improve model convergence and stability during training 💡 5. Incremental Learning Scheme: The learning approach leverages information from both an initial model and subsequent updated models to regularize and avoid catastrophic forgetting, improving metrics while reducing training times 📈 6. Multi-Task Learning Architectures: Various MTL architectures for improving ranking by simultaneously optimizing like engagement, relevance and personalization are explored. A grouping strategy is found to provide good improvements with minimal parameter increases 🔑 7. Practical Training Optimization: Multiple training optimizations are discussed and benchmarked, including model parallelism, optimized data loading and computational graph splitting ⏰ 8. Production Deployment Enhancements: Techniques like quantization and vocabulary compression using QR-hashing are proposed to reduce the size of large models and streamline their deployment 📦 Insights presented in the paper could benefit other practitioners working with large-scale industrial recommendation systems. Link in the comments! #recommendersystems #deeplearning #machinelearning #personalization #ranking

Dear Data Scientists, Today we are doing a deep dive into production-ready recommender systems in Python. Here is my practical guide to the top Python libraries that truly deliver value: 1. Surprise (scikit-surprise) --> https://surpriselib.com/ - Implements classic algorithms like SVD, SVD++, and neighborhood-based approaches - Clean scikit-learn style API with built-in cross-validation - Perfect for explicit feedback (ratings) scenarios - Can be slow on large datasets but excellent for prototyping - Best for: Traditional recommendation problems where you have explicit user ratings 2. LightFM --> https://lnkd.in/dGif8QWU - Hybrid recommender combining collaborative and content-based filtering - Uses learned embeddings with WARP loss - Handles both implicit and explicit feedback - Excellent scalability with fast C++ backend - Best for: When you have rich user/item metadata and need production-grade performance 3. Implicit --> https://lnkd.in/dpSGnquV - Specialized in implicit feedback (clicks, views, purchases) - Implements ALS, BPR, and approximate nearest neighbors - Lightning-fast with GPU support - Limited to pure collaborative filtering - Best for: Large-scale implicit feedback scenarios where speed is crucial 4. TensorFlow Recommenders --> https://lnkd.in/dvuhCAkV - Official TensorFlow library for building recommender systems - Pre-built modules for two-tower models, retrieval, and ranking - Implements modern approaches like DCN v2 and multiple loss objectives - Seamless integration with TensorFlow ecosystem and feature preprocessing - Highly scalable with production-ready serving via TensorFlow Serving - Best for: Enterprise-scale recommendations where you need end-to-end ML pipeline integration and when serving latency is critical 5. Spotlight --> https://lnkd.in/duQeK73D - PyTorch-based with factorization and sequence models - Great for sequential recommendation problems - Implements modern approaches like neural collaborative filtering - Active development but smaller community - Best for: Sequential recommendations (e.g., next item prediction) and when you want PyTorch integration My advice: Start with Surprise for proof of concepts. Move to LightFM if you have rich metadata. Choose Implicit for large-scale deployment with implicit feedback. Consider TensorFlow Recommenders or Spotlight when you need deep learning capabilities or sequential modeling. Agree? What is your go-to recommender library? Let's discuss in the comments! #DataScience #DataScientist #Data #ML #MachineLearning #RecommenderSystems #Python #AI #Recommender #Recommendation #DataAnalytics #DataAnalyst #DataEngineer #DataCareers #DataJobs #CareerProgression #ProfessionalDevelopment #DataScienceMentorship #Mentorship #Learning #Portfolio #DataPortfolio #TechJobs #TechIndustry #IndustryStandards #BestPractices #LightFM #Surprise #Spotlight #Implicit #TensorFlow #TensorFlowRecommenders #PyTorch

When developing a recommendation system for industrial settings, it's crucial to account for the constraints posed by extensive item catalogs. A common approach to address the challenges of large item corpora involves employing a two-stage methodology. This approach divides the recommendation process into two distinct phases: candidate retrieval and ranking. This blog, written by machine learning scientists at Expedia Group, shares their insights on harnessing the power of a Two Tower Neural Network architecture to enhance candidate retrieval modeling. The "two tower" architecture has proven successful in various recommendation scenarios across diverse domains, demonstrating particular efficacy in handling vast industrial product catalogs. At its core, the architecture comprises a "query encoder" and an "item encoder." The output of each encoder undergoes interaction via a dot product before being fed into an activation function like SoftMax or Sigmoid. The query encoder learns a representation from features such as search queries, reference items, user historical interactions, or any context relevant to the user and their search. Conversely, the item encoder processes the candidate item, typically representing it through content features like property location, popularity-based attributes, and property amenities in the case of Expedia lodging. The author provides a comprehensive, step-by-step guide on implementing the two-tower neural network in TensorFlow, accompanied by additional techniques to enhance its performance. This resource serves as a valuable reference for those working on recommendation systems. #machinelearning #algorithms #recommendations #retrieval #ranking  – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Spotify: https://lnkd.in/gKgaMvbh https://lnkd.in/gjuG3xhJ

Algorithmic merchandising was our catalyst for a 62% increase in revenue – with the same traffic. Here's our crazy experiment👇 We ran a crazy experiment over the last couple of weeks. While analyzing the data to find the next big growth lever for one of our longest-standing brands I’ve noticed something interesting. Over 32% of the site-wide traffic was hitting collection pages. Also, I identified some outperforming products (hidden champions) that were getting a lot of clicks even though they weren't in prime positions. On the other hand, some products that were getting the most impressions weren't performing as well. People stopped browsing more often when there were a lot of poor performing products in the visible space. So good products didn't even get a chance to be shown to many people. What if we could change the allocation of these products? – Give good products more visibility and bad products less. The challenge now was to find those outliers and position them accordingly. The real breakthrough came when I figured out how to use this data to improve product placement on collection pages. My approach went beyond just tracking clicks. I looked at several key metrics to get a full picture of how each product is doing: → CTR by position → Basket Rate → Purchase Rate: → 90-day Product LTV These 4 indicators were fed into RetentionX's machine learning process to generate a performance indicator that creates a score from 0-100. Products that weren’t performing as well in their current spots were moved to less prominent positions, freeing up space for the real stars — the products that were outperforming expectations. For the first time, our customer had a clear strategy for how to present their products, one that went beyond just gut feelings and good looks. They could now combine our automated insights with their own logic for sorting products—like aligning email campaigns with what customers would see on the site, push new arrivals and demote low stock items. The changes we made had a noticeable impact. Collection pages, which had been somewhat overlooked, suddenly became the go-to place to track what was happening with their customers and how their products were being perceived. The numbers told us we were on the right track, and remember this is a $40M+ brand: → 62% More Profit from the Same Traffic → 27% Additional Increase in Revenue → 23% Higher Conversion Rate → 12% Increase in AOV → 18% Increase in Basket Rates When we saw how well this approach worked, we knew we couldn't keep it to ourselves. So Merchandise Automation is now part of our RetentionX Core product. Read the full case study here: https://lnkd.in/dHh_Sbkp

Building a Recommender System Project That Actually Impresses Recruiters One of the best datasets for RecSys would be H&M Personalized Fashion Recommendations dataset on Kaggle - https://lnkd.in/ggENFKJh It’s rich, messy, and realistic. Here’s how I’d approach building a recommender system project from scratch 🧩 𝐃𝐚𝐭𝐚𝐬𝐞𝐭 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 & 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 The H&M dataset gives you: 🧍♀️ Customer data (age, club membership, fashion style preferences) 👕 Article data (product type, color, index groups, etc.) 🕓 Transaction data (what was purchased and when) The goal? Predict what a customer will buy next. You can create features like: 👉 Customer-level: Recency, frequency, and monetary value (RFM), brand affinity, preferred product types. 👉 Item-level: Product embeddings from text or image features. 👉 Interaction-level: Time since last purchase, co-purchase patterns, session-based behavior. Feature engineering is your chance to show real-world problem-solving beyond code. ⚙️ 𝐌𝐨𝐝𝐞𝐥 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 & 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 Start simple → go complex. ✅ Baseline models - Popularity-based recommendations, Item-item or user-user collaborative filtering ✅ Advanced models - Matrix Factorization (ALS, SVD++), Deep Learning like Neural Collaborative Filtering, Transformers for sequential recommendations, Gradient Boosting on handcrafted features Metrics also matter and recruiters notice when you choose the right ones. Precision@K / Recall@K for relevance, MAP@K (used in the Kaggle competition), Diversity and novelty metrics for business realism 🎛️ 𝐇𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫 𝐓𝐮𝐧𝐢𝐧𝐠 & 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Don’t just fit a model, you have to optimize it. Use techniques like - GridSearchCV or Optuna for efficient search, Bayesian Optimization for deep models, Learning rate schedules, regularization tuning etc. Document the trade-offs you make and provide resoning behind choices! 🔍 𝐄𝐫𝐫𝐨𝐫 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 & 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 After the first model run, your job isn’t done. A few thinks to ask would be - 👉 Which users are we failing for (cold-start customers)? 👉 Are certain product types over-recommended? 👉 Does the model overfit to recent trends? Then, iterate: add new features, re-sample data, or blend multiple recommenders. This error analysis and improving the model is what really stands out, and most of what you do in your day to day as well. A well-crafted recommender system project like this can demonstrate the full lifecycle of a Data Scientist — from raw data to business value A few final tips: When you present this project on GitHub - ✅ Explain your design choices clearly in the README.md ✅ Visualize recommendations (e.g., “If you liked this, you might like…”).