Articoli correlati a Modeling Noise in Recommender Systems
Sinossi
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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Risultati della ricerca per Modeling Noise in Recommender Systems
Foto dell'editore
Modeling Noise in Recommender Systems (Paperback)
Print on DemandDa: Grand Eagle Retail, Bensenville, IL, U.S.A.
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: 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. Codice articolo 9798256295981
Compra nuovo
EUR 29,41
Spedizione gratuita
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: 1 disponibili
Foto dell'editore
Modeling Noise in Recommender Systems
Da: California Books, Miami, FL, U.S.A.
Valutazione del venditore 5 su 5 stelle
Condizione: New. Codice articolo I-9798256295981
Compra nuovo
EUR 29,42
Spedizione gratuita
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: Più di 20 disponibili
Foto dell'editore
Modeling Noise in Recommender Systems
Da: PBShop.store US, Wood Dale, IL, U.S.A.
Valutazione del venditore 5 su 5 stelle
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo L2-9798256295981
Compra nuovo
EUR 30,54
Spedizione gratuita
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: Più di 20 disponibili
Foto dell'editore
Modeling Noise in Recommender Systems
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
Valutazione del venditore 5 su 5 stelle
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo L2-9798256295981
Compra nuovo
EUR 28,33
Spedizione EUR 3,89
Spedito da Regno Unito a U.S.A.
Spedito da Regno Unito a U.S.A.
Quantità: Più di 20 disponibili
Foto dell'editore
Modeling Noise in Recommender Systems
Print on DemandDa: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 150 pp. Englisch. Codice articolo 9798256295981
Compra nuovo
EUR 32,30
Spedizione EUR 23,00
Spedito da Germania a U.S.A.
Spedito da Germania a U.S.A.
Quantità: 2 disponibili
Foto dell'editore
Modeling Noise in Recommender Systems
Print on DemandDa: AHA-BUCH GmbH, Einbeck, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: 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. Codice articolo 9798256295981
Compra nuovo
EUR 38,53
Spedizione EUR 35,00
Spedito da Germania a U.S.A.
Spedito da Germania a U.S.A.
Quantità: 2 disponibili
Foto dell'editore
Modeling Noise in Recommender Systems (Paperback)
Print on DemandDa: CitiRetail, Stevenage, Regno Unito
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: 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. Codice articolo 9798256295981
Compra nuovo
EUR 32,84
Spedizione EUR 43,71
Spedito da Regno Unito a U.S.A.
Spedito da Regno Unito a U.S.A.
Quantità: 1 disponibili
Foto dell'editore
Modeling Noise in Recommender Systems (Paperback)
Print on DemandDa: AussieBookSeller, Truganina, VIC, Australia
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: 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. Codice articolo 9798256295981
Compra nuovo
EUR 45,22
Spedizione EUR 33,03
Spedito da Australia a U.S.A.
Spedito da Australia a U.S.A.
Quantità: 1 disponibili
Immagini fornite dal venditore
Modeling Noise in Recommender Systems
Print on DemandDa: preigu, Osnabrück, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: 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. Codice articolo 136318672
Compra nuovo
EUR 33,30
Spedizione EUR 70,00
Spedito da Germania a U.S.A.
Spedito da Germania a U.S.A.
Quantità: 5 disponibili