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Recommender systems /
Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the...
| Clasificación: | Libro Electrónico |
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| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London : Hoboken, NJ :
ISTE ; Wiley,
2014.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Recommender systems / |c edited by Gérald Kembellec, Ghislaine Chartron, Imad Saleh. |
| 264 | 1 | |a London : |b ISTE ; |a Hoboken, NJ : |b Wiley, |c 2014. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (John Wiley, viewed December 15, 2014). | |
| 505 | 0 | |a Cover; Title Page; Copyright; Contents; Preface: Recommender Engines (and Systems); Acknowledgments; Bibliography; 1: General Introduction to Recommender Systems; 1.1. Putting it into perspective; 1.2. An interdisciplinary subject; 1.3. The fundamentals of algorithms; 1.3.1. Collaborative filtering; 1.3.1.1. Advantages and drawbacks of collaborative filtering; 1.3.2. Content filtering; 1.3.2.1. Advantages and drawbacks of content filtering; 1.3.3. Hybrid methods; 1.3.4. Conclusion on historical recommendation models; 1.4. Content offers and recommender systems. | |
| 505 | 8 | |a 1.4.1. Culture and recommender systems1.4.1.1. Recommendation and cinema; 1.4.1.2. Recommendation and literature; 1.4.1.3. Recommendation and general culture; 1.4.2. Recommender systems and the e-commerce of content; 1.4.3. The behavior of users; 1.5. Current issues; 1.6. Bibliography; 2: Understanding Users' Expectations for Recommender Systems: the Case of Social Media; 2.1. Introduction: the omnipresence of recommender systems; 2.2. The social approach to prescription; 2.2.1. The theory of the prescription and online interactions; 2.2.2. Conditions for recognition of the prescription. | |
| 505 | 8 | |a 2.2.3. The specificities of social media2.3. Users who do not focus on the prescriptions of platforms; 2.3.1. Facebook: the link, the type of activity and the context; 2.3.2. Twitter: prescription between peers and explanation of prescription; 2.3.3. Conditions for the recognition of a prescription: announcement and enunciation; 2.4. A guide for considering recommender systems adapted to different forms of social media; 2.5. Conclusion; 2.6. Bibliography; 3: Recommender Systems and Social Networks: What Are the Implications for Digital Marketing? | |
| 505 | 8 | |a 3.1. Social recommendations: an ancient practice revived by the digital age3.1.1. Recommendations: a difficult management for brands; 3.1.2. Internet recommendations: social presence and personalized recommendations; 3.2. Social recommendations: how are they used for e-commerce?; 3.2.1. Efficiency of recommender systems with regard to the performance of e-commerce websites; 3.2.2. Recommender systems used by social networks: from e-commerce to social commerce; 3.2.2.1. Facebook, innovator in its vision for social recommendation: Like, Edge Rank, Place, Social and Open Graph. | |
| 505 | 8 | |a 3.2.2.2. Social recommendation, the cornerstone of an emerging social commerce3.3. Conclusion; 3.4. Bibliography; 4: Recommender Systems and Diversity: Taking Advantage of the Long Tail and the Diversity of Recommendation Lists; 4.1. The stakes associated with diversity within recommender systems; 4.1.1. Individual diversity or the individual perception of diversity; 4.1.2. The stakes and impacts of aggregate diversity; 4.1.2.1. Markets with limited resources; 4.1.2.2. Cultural diversity; 4.1.2.3. The long-tail economy: toward a more diverse consumption. | |
| 520 | |a Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales. On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understan. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Recommender systems (Information filtering) | |
| 650 | 6 | |a Systèmes de recommandation (Filtrage d'information) | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Recommender systems (Information filtering) |2 fast | |
| 700 | 1 | |a Kembellec, Gérald, |e editor. | |
| 700 | 1 | |a Chartron, Ghislaine, |e editor. | |
| 700 | 1 | |a Saleh, Imad, |d 1960- |e editor. |1 https://id.oclc.org/worldcat/entity/E39PCjCKxjQYp3Qc9hVDT6TwVK | |
| 758 | |i has work: |a Recommender systems (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFKJQMtDFtKJfq4gP8PQpX |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |t Recommender systems. |d London, [England] ; Hoboken, New Jersey : ISTE Limited : John Wiley & Sons, ©2014 |h xvi, 232 pages |z 9781848217683 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1884200 |z Texto completo |
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