Recommender System Principles Technologies And Enterprise Applications A Practical Deep Dive For Business Pros And AI Fans Looking To Level Up Industry Tech For Modern Biz Embracing Data Driven Personalization And Smart Automation Across Sectors

Practical Guide to Enterprise Recommender Systems and AI
✔️ 1-volume edition, 261 pages, English, data-driven insights

This book delivers a practical, industry-focused deep dive into recommender systems, covering core modules from content understanding to ranking, with emphasis on enterprise deployment and real-world use cases. It’s ideal for professionals in recommender systems, computational advertising, and search, as well as students in AI, computer science, software engineering seeking a solid grounding in modern, data-driven personalization and smart automation across sectors.

✅ Pages: 261
✅ Language: English
✅ Release date: 2025-02-06
✅ Core topics: content understanding, user profiling, recall, ranking, re-ranking
✅ Technologies covered: reinforcement learning, causal inference
✅ Audience: professionals and students in AI, CS, and software engineering
✅ Focus: cold start and debiasing challenges

💡 What is a practical approach to implementing enterprise recommender systems in business? An effective approach blends solid data foundations with scalable delivery.

- Data quality and governance for reliable outputs - Modular architecture with core modules like content understanding, user profiling, recall, and ranking - Monitoring for bias, drift, and feedback loops - Controlled experimentation and phased rollout to minimize risk