Recommender

This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.

Use collaborative filtering to recommend movies to users! Implementations step by step from scratch!

Recommender Engines are taking the world by storm as they automate the process of recommending by quickly learning from the data. All of this has been done using machine learning .

Build Recommender Systems for Real World Applications using Machine Learning

Recommender Systems: Build a Customized Recommendation System or Recommendation engine using Deep Learning

How to create machine learning recommendation systems with deep learning, collaborative filtering, and Python.