This book presents a multi-disciplinary approach for development of Recommender Systems. It explains different types of pertinent algorithms with their comparative analysis, and their role for different applications including case studies. It explains Big Data behind Recommender System, making good decision support systems, etc.
This book presents a multi-disciplinary approach for development of Recommender Systems. It explains different types of pertinent algorithms with their comparative analysis, and their role for different applications including case studies. It explains Big Data behind Recommender System, making good decision support systems, etc.
1. Comparison of Different Machine Learning Algorithms to Classify Whether or Not a Tweet Is about a Natural Disaster: A Simulation-Based Approach; 2. An End-to-End Comparison among Contemporary Content-Based Recommendation Methodologies; 3. Neural Network-Based Collaborative Filtering for Recommender Systems; 4. Recommendation System and Big Data: Its Types and Applications; 5. The Role of Machine Learning /AI in Recommender Systems; 6. A Recommender System Based on TensorFlow Framework; 7. A Marketing Approach to Recommender Systems; 8. Applied Statistical Analysis in Recommendation Systems; 9. An IoT-Enabled Innovative Smart Parking Recommender Approach; 10. Classification of Road Segments in Intelligent Traffic Management System; 11. Facial Gestures-Based Recommender System for Evaluating Online Classes; 12. Application of Swarm Intelligence in Recommender Systems; 13. Application of Machine-Learning Techniques in the Development of Neighbourhood-Based Robust Recommender Systems; 14. Recommendation Systems for Choosing Online Learning Resources: A Hands-On Approach
1. Comparison of Different Machine Learning Algorithms to Classify Whether or Not a Tweet Is about a Natural Disaster: A Simulation-Based Approach; 2. An End-to-End Comparison among Contemporary Content-Based Recommendation Methodologies; 3. Neural Network-Based Collaborative Filtering for Recommender Systems; 4. Recommendation System and Big Data: Its Types and Applications; 5. The Role of Machine Learning /AI in Recommender Systems; 6. A Recommender System Based on TensorFlow Framework; 7. A Marketing Approach to Recommender Systems; 8. Applied Statistical Analysis in Recommendation Systems; 9. An IoT-Enabled Innovative Smart Parking Recommender Approach; 10. Classification of Road Segments in Intelligent Traffic Management System; 11. Facial Gestures-Based Recommender System for Evaluating Online Classes; 12. Application of Swarm Intelligence in Recommender Systems; 13. Application of Machine-Learning Techniques in the Development of Neighbourhood-Based Robust Recommender Systems; 14. Recommendation Systems for Choosing Online Learning Resources: A Hands-On Approach
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