{"product_id":"ml-in-retail-personalizing-shopping-experiences-implement-ml-for-recommendation-systems-and-inventory-optimization-9798265144096","title":"ML in Retail Personalizing Shopping Experiences: Implement ML for recommendation systems and inventory optimization","description":"\u003cp\u003e • Author(s): Isandro Myles\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Industries - Retailing\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eEvery click, cart, and return tells a story. \u003cb\u003eML in Retail: Personalizing Shopping Experiences\u003c\/b\u003e shows you how to turn retail data into smarter recommendations, sharper pricing, and inventory that's right where customers need it-online and in-store. No hype-just the proven patterns that teams use to build ML features shoppers actually feel.\u003c\/p\u003e\u003cp\u003eYou'll design a retail ML stack end-to-end: from clean product and event data to feature stores, training, and real-time serving. Build recommenders that combine collaborative filtering, graph signals, and sequence models, then re-rank for context and business rules. Pair demand forecasting with allocation and replenishment so shelves stay stocked without overbuying. Wrap it all with A\/B testing, guardrails, and MLOps so wins are repeatable and safe.\u003c\/p\u003e\u003cp\u003eYou'll learn how to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003eBuild modern recommendation systems (user-item CF, graph\/sequence models, contextual re-ranking)\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003ePersonalize experiences with segments, real-time context, bandits, and constraints (diversity, margin, exposure)\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eImprove search \u0026amp; discovery with embeddings, vector search, and learning-to-rank\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eModel price \u0026amp; promo effects (elasticity, uplift) and run sound experiments\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eForecast demand and optimize inventory, allocation, and markdowns\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eOperate at scale with data quality checks, drift monitoring, online features, and safe rollouts\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eAddress trust, privacy, and bias with transparent metrics and governance\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eWritten for data scientists, ML engineers, analysts, and product leaders, this is your practical playbook for measurable, customer-friendly retail ML.\u003c\/p\u003e","brand":"Atlantic Books","offers":[{"title":"Paperback","offer_id":46332852142231,"sku":"9798265144096","price":1654.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798265144096.webp?v=1767715523","url":"https:\/\/atlanticbooks.com\/products\/ml-in-retail-personalizing-shopping-experiences-implement-ml-for-recommendation-systems-and-inventory-optimization-9798265144096","provider":"Atlantic Books","version":"1.0","type":"link"}