Recommender Systems
portes grátis
Recommender Systems
A Multi-Disciplinary Approach
Datta, Sujoy; Roy, Monideepa; Kar, Pushpendu
Taylor & Francis Ltd
12/2024
260
Mole
9781032333229
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Algorithms;User Response;Data Mining;Consumar Behaviour;Decision Support Systems;Machine Learning/AI;Recommender System;MIT Open Courseware;TPB Factor;Smart Phone;CF;Content Based Recommender Systems;Hybrid Recommender System;RSU;IoT Device;Deep Neural Networks;Cf Method;Cold Start Problem;Support Vector Machine;Smart Parking;Data Set;Road Segments;Road Traffic Data;Data Processing Module;PSO Technique;Rec;Roc Curve;Facial Landmarks;Gaussian Mixture Model;Web App;Embedding Vectors
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Algorithms;User Response;Data Mining;Consumar Behaviour;Decision Support Systems;Machine Learning/AI;Recommender System;MIT Open Courseware;TPB Factor;Smart Phone;CF;Content Based Recommender Systems;Hybrid Recommender System;RSU;IoT Device;Deep Neural Networks;Cf Method;Cold Start Problem;Support Vector Machine;Smart Parking;Data Set;Road Segments;Road Traffic Data;Data Processing Module;PSO Technique;Rec;Roc Curve;Facial Landmarks;Gaussian Mixture Model;Web App;Embedding Vectors
