ISBN: 9798256295981 - modeling noise in recommender systems (9 resultados)

Condición: New.

Paperback. Condición: new. Paperback. Recommender systems play an important role in modern digital platforms by helping users discover products, services, media, information, and other forms of personalized content. Their effectiveness depends not only on the quality of recommendation algorithms but also on the reliability of the data used to train and evaluate them. Modeling Noise in Recommender Systems provides a focused technical examination of noise, uncertainty, imperfect observations, and data quality within recommendation environments. The book connects recommender systems, machine learning, data mining, statistical modeling, information retrieval, and computational intelligence within a structured framework.The book introduces the fundamental concepts behind recommender systems and examines how user behavior and interaction data are represented computationally. Topics such as ratings, implicit feedback, user-item interactions, preference modeling, similarity measures, collaborative filtering, content-based recommendation, and predictive modeling provide the foundation for understanding how recommendation algorithms operate.A central focus is placed on noise and uncertainty within recommender-system data. User ratings and behavioral signals may contain inconsistencies, missing information, accidental interactions, biased observations, or other forms of uncertainty. The book considers how such imperfections can affect model training, prediction accuracy, ranking, personalization, and evaluation. Understanding the sources and characteristics of noise is therefore important when designing reliable recommendation models.The book further explores approaches for representing and modeling noisy observations. Statistical methods, probabilistic approaches, robust modeling concepts, data preprocessing, outlier handling, uncertainty estimation, and noise-aware learning are considered within the broader context of recommendation algorithms. These approaches provide a foundation for understanding how recommendation models can account for imperfect data rather than treating every observed interaction as equally reliable.Attention is also given to the relationship between noisy data and recommender-system performance. Evaluation measures, prediction errors, ranking quality, robustness, generalization, and sensitivity to data perturbations are discussed as important considerations when assessing recommendation models. The book emphasizes the importance of distinguishing genuine preference signals from unreliable or ambiguous observations. Modeling Noise in Recommender Systems examines how imperfect observations, uncertainty, inconsistent feedback, and noisy data can influence recommendation algorithms. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 150 pp. Englisch.

Paperback. Condición: new. Paperback. Recommender systems play an important role in modern digital platforms by helping users discover products, services, media, information, and other forms of personalized content. Their effectiveness depends not only on the quality of recommendation algorithms but also on the reliability of the data used to train and evaluate them. Modeling Noise in Recommender Systems provides a focused technical examination of noise, uncertainty, imperfect observations, and data quality within recommendation environments. The book connects recommender systems, machine learning, data mining, statistical modeling, information retrieval, and computational intelligence within a structured framework.The book introduces the fundamental concepts behind recommender systems and examines how user behavior and interaction data are represented computationally. Topics such as ratings, implicit feedback, user-item interactions, preference modeling, similarity measures, collaborative filtering, content-based recommendation, and predictive modeling provide the foundation for understanding how recommendation algorithms operate.A central focus is placed on noise and uncertainty within recommender-system data. User ratings and behavioral signals may contain inconsistencies, missing information, accidental interactions, biased observations, or other forms of uncertainty. The book considers how such imperfections can affect model training, prediction accuracy, ranking, personalization, and evaluation. Understanding the sources and characteristics of noise is therefore important when designing reliable recommendation models.The book further explores approaches for representing and modeling noisy observations. Statistical methods, probabilistic approaches, robust modeling concepts, data preprocessing, outlier handling, uncertainty estimation, and noise-aware learning are considered within the broader context of recommendation algorithms. These approaches provide a foundation for understanding how recommendation models can account for imperfect data rather than treating every observed interaction as equally reliable.Attention is also given to the relationship between noisy data and recommender-system performance. Evaluation measures, prediction errors, ranking quality, robustness, generalization, and sensitivity to data perturbations are discussed as important considerations when assessing recommendation models. The book emphasizes the importance of distinguishing genuine preference signals from unreliable or ambiguous observations. Modeling Noise in Recommender Systems examines how imperfect observations, uncertainty, inconsistent feedback, and noisy data can influence recommendation algorithms. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condición: new. Paperback. Recommender systems play an important role in modern digital platforms by helping users discover products, services, media, information, and other forms of personalized content. Their effectiveness depends not only on the quality of recommendation algorithms but also on the reliability of the data used to train and evaluate them. Modeling Noise in Recommender Systems provides a focused technical examination of noise, uncertainty, imperfect observations, and data quality within recommendation environments. The book connects recommender systems, machine learning, data mining, statistical modeling, information retrieval, and computational intelligence within a structured framework.The book introduces the fundamental concepts behind recommender systems and examines how user behavior and interaction data are represented computationally. Topics such as ratings, implicit feedback, user-item interactions, preference modeling, similarity measures, collaborative filtering, content-based recommendation, and predictive modeling provide the foundation for understanding how recommendation algorithms operate.A central focus is placed on noise and uncertainty within recommender-system data. User ratings and behavioral signals may contain inconsistencies, missing information, accidental interactions, biased observations, or other forms of uncertainty. The book considers how such imperfections can affect model training, prediction accuracy, ranking, personalization, and evaluation. Understanding the sources and characteristics of noise is therefore important when designing reliable recommendation models.The book further explores approaches for representing and modeling noisy observations. Statistical methods, probabilistic approaches, robust modeling concepts, data preprocessing, outlier handling, uncertainty estimation, and noise-aware learning are considered within the broader context of recommendation algorithms. These approaches provide a foundation for understanding how recommendation models can account for imperfect data rather than treating every observed interaction as equally reliable.Attention is also given to the relationship between noisy data and recommender-system performance. Evaluation measures, prediction errors, ranking quality, robustness, generalization, and sensitivity to data perturbations are discussed as important considerations when assessing recommendation models. The book emphasizes the importance of distinguishing genuine preference signals from unreliable or ambiguous observations. Modeling Noise in Recommender Systems examines how imperfect observations, uncertainty, inconsistent feedback, and noisy data can influence recommendation algorithms. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Recommender systems play an important role in modern digital platforms by helping users discover products, services, media, information, and other forms of personalized content. Their effectiveness depends not only on the quality of recommendation algorithms but also on the reliability of the data used to train and evaluate them. Modeling Noise in Recommender Systems provides a focused technical examination of noise, uncertainty, imperfect observations, and data quality within recommendation environments. The book connects recommender systems, machine learning, data mining, statistical modeling, information retrieval, and computational intelligence within a structured framework.The book introduces the fundamental concepts behind recommender systems and examines how user behavior and interaction data are represented computationally. Topics such as ratings, implicit feedback, user-item interactions, preference modeling, similarity measures, collaborative filtering, content-based recommendation, and predictive modeling provide the foundation for understanding how recommendation algorithms operate.A central focus is placed on noise and uncertainty within recommender-system data. User ratings and behavioral signals may contain inconsistencies, missing information, accidental interactions, biased observations, or other forms of uncertainty. The book considers how such imperfections can affect model training, prediction accuracy, ranking, personalization, and evaluation. Understanding the sources and characteristics of noise is therefore important when designing reliable recommendation models.The book further explores approaches for representing and modeling noisy observations. Statistical methods, probabilistic approaches, robust modeling concepts, data preprocessing, outlier handling, uncertainty estimation, and noise-aware learning are considered within the broader context of recommendation algorithms. These approaches provide a foundation for understanding how recommendation models can account for imperfect data rather than treating every observed interaction as equally reliable.Attention is also given to the relationship between noisy data and recommender-system performance. Evaluation measures, prediction errors, ranking quality, robustness, generalization, and sensitivity to data perturbations are discussed as important considerations when assessing recommendation models. The book emphasizes the importance of distinguishing genuine preference signals from unreliable or ambiguous observations.…

Taschenbuch. Condición: Neu. Modeling Noise in Recommender Systems | Tom Ford | Taschenbuch | Englisch | 2026 | Beakers Bay | EAN 9798256295981 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.