Data Mining for Product Suggestions

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 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

Amazon’s success isn’t just about having everything in one place. It’s about knowing what you want—often before you do. By analyzing user behavior, preferences, and past purchases, Amazon created a recommendation engine that feels personal to each shopper. But how did they do it? -Recommendation Engine: Every time you search, browse, or purchase, Amazon tracks your behavior. Their sophisticated algorithms analyze this data to create personalized product suggestions. This not only increases sales but also keeps customers engaged by showing them exactly what they’re looking for—even when they didn’t know it. -Customer Segmentation: Amazon divides its vast customer base into micro-segments based on preferences, buying history, and even browsing time. This allows them to target customers with highly relevant offers, email suggestions, and promotions. It’s not just mass marketing—it’s targeted, personalized marketing. -Anticipatory Shipping: Using predictive analytics, Amazon can forecast what products customers are likely to order soon and ship them to nearby fulfillment centers before an order is placed. This cuts down delivery time significantly and enhances customer satisfaction. Their data insights predict trends even on a micro scale—right down to individual customer needs. -Product Reviews & Feedback: Amazon uses customer reviews and ratings not just for quality control, but also to shape future recommendations. Negative feedback helps the algorithm refine suggestions, while positive feedback boosts product visibility. They’ve turned the review system into another data source to further enhance personalization. -Dynamic Pricing: Using data on demand, competitor prices, and buying trends, Amazon can adjust product prices in real-time. This ensures customers are seeing competitive prices and gives Amazon an edge in retaining price-conscious shoppers. What can your business learn from this? -Leverage customer data to understand behavior: By tracking user actions on your website or app, you can tailor their experience, just like Amazon. -Segment your audience: Identify patterns within your customer base and target each group with content or offers that speak directly to their needs. -Predict customer needs: Use data to anticipate what your customers want before they even ask for it, ensuring faster service and more relevant offerings. Key insight: Data isn’t just numbers—it’s the foundation for a personalized, optimized experience that keeps customers coming back for more.  

Let's build a Recommender for an E-Commerce clothing site from scratch. 🛍️📈 This notebook shows how to deliver personalized, scalable recommendations even in cold-start scenarios. 👉 Product details include: - Price, - Rating, - Category, - Description, - Number of reviews, - Product name with brand. We have two user types, defined by their initial product choice at registration or general preferences around price range and review requirements. We'll use the Superlinked Framework to combine product and user data to deliver personalized recommendations at scale. Let's dive in 🏗️: 1️⃣ Data Preparation ⇒ Load and preprocess product and user data. 2️⃣ Set up the Recommender System ⇒ Define schemas for products, users, and user-product interactions. ⇒ Create embedding spaces for different data types to enable similarity retrieval. ⇒ Create the index, combining embedding spaces with adjustable weights to prioritize desired characteristics. 3️⃣ Cold-Start Recommendations ⇒ For new users without behavior data, we'll base recommendations on their initial product choice or general preferences, ensuring they're never left in the cold. 4️⃣ Incorporate User Behavior Data ⇒ Introduce user behavior data such as clicked, purchased, and added to the cart with weights indicating interest level. ⇒ Update the index to capture the effects of user behavior on text similarity spaces. 5️⃣ Personalized Recommendations ⇒ Now it's time to tailor recommendations based on user preferences and behavior data. ⇒ Compare personalized recommendations to cold-start recommendations to highlight the impact of behavior data. Ant that's a wrap! 🔁 Adjusting weights allows you to control the importance assigned to each characteristic in the final index. This tailors recommendations to desired behavior while keeping them fresh and relevant... it's easier than chasing the latest fashion trends. ✨ Dig into the notebook to implement this approach 👉 https://lnkd.in/edeQW344 Why not show some support by starring our repo? ⭐️ We'd appreciate it more than a free fashion consultation! 😉

The recommendation is a powerful tool for e-commerce sites to boost sales by helping customers discover relevant products and encouraging additional purchases. By offering well-curated product bundles and personalized suggestions, these platforms can improve the customer experience and drive higher conversion rates. In a recent blog post, the CVS Health data science team shares how they explore advanced machine learning capabilities to develop new recommendation prototypes. Their objective is to create high-quality product bundles, making it easier for customers to select complementary products to purchase together. For instance, bundles like a “Travel Kit” with a neck pillow, travel adapter, and toiletries can simplify purchasing decisions. The implementation includes several components, with a key part being the creation of product embeddings using a Graph Neural Network (GNN) to represent product similarity. Notably, rather than using traditional co-view or co-purchase data, the team leveraged GPT-4 to directly identify the top complementary segments as labels for the GNN model. This approach has proven effective in improving recommendation accuracy. With these product embeddings in place, the bundle recommendations are further refined by incorporating user-specific data based on recent purchase patterns, resulting in more personalized suggestions. As large language models (LLMs) become increasingly adept at mimicking human decision-making, using them to enhance labeling quality and streamline insights in machine learning workflows is becoming more popular. For those interested, this is an excellent case study to explore. #machinelearning #datascience #ChatGPT #LLMs #recommendation #personalization #SnacksWeeklyOnDataScience – – –  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:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gb6UPaFA

Data Science Interview Question: Design a recommender system to capture both long and short preference of users Real production recommenders are almost always hybrids: a fast “coarse” stage that captures long-term preferences of the user and fetches a few hundred candidates, and a smarter “fine” stage that reads the user’s most recent behavior to decide their internt and figure out what to show now. Now within each, what we choose would depend on sparse the data, latency requirements etc. So its best to ask more questions to the interviewer and get more details. One possible architecture is - Two-tower retrieval + Transformer network for retreival. Lets dive into the how this works. A two-tower model (user tower, item tower) with a dot-product or cosine similarity head is still the most reliable way to encode preferences and serve them fast via ANN. The user tower ingests static and slowly varying signals like profile demographics, long-horizon aggregates, historical topic mixture, embeddings of past items compressed with attention pooling. The item tower ingests titles, taxonomies, content embeddings, and seller/creator metadata. Trained with BPR-style losses and hard negative mining, you get well-separated embedding spaces that support <10ms retrieval via approximate nearest neighbor search (ANN).  From a sparsity perspective, two-tower is robust because you can lean on side information. Cold-start items get reasonable vectors from content encoders, and cold-start users inherit from cohort or context features. This makes it ideal as a candidate generator. Transformers shine at reading the order and timing of a user’s last N actions. Architectures like GRU4Rec, SASRec, DIN etc model the immediate session state i.e. what the user is trying to accomplish now. These models are best used in the ranking stage, where the candidate set is reduced to a few hundred items. That keeps compute as full self-attention over dozens of events will take time. Additional Improvement - GNN! Sparsity in interaction data hampers both retrieval and ranking. If you map out your catalog data as a graph then GNNs (LightGCN or PinSage) can propagate signals over the user–item–attribute graph so that even lightly interacted nodes inherit useful representations from their neighbors. This is particularly effective for cold-start items, niche creators, and fresh inventory. -- 🚶➡️ To land your next Data Science role, follow me - Karun! ♻️ Share so others can learn, and you can build your LinkedIn presence! 📕 For more questions, grab a copy of Decoding ML Interviews - https://lnkd.in/gc76-4eP