Realtime data mining : self-learning techniques for recommendation angines
Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore...
Enregistré dans:
| Hovedforfatter: | |
|---|---|
| Format: | Livre numérique |
| Sprog: | Anglais |
| Udgivet: |
Cham :
Springer International Publishing
[20..].
Cham : Springer Nature |
| Udgivelse: | 1st ed. 2013. |
| Serier: | Applied and Numerical Harmonic Analysis
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| Online adgang: | Accès sur la plateforme de l'éditeur (Springer) Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Realtime Data Mining, Texte imprimé, 9783319013206 • Realtime Data Mining, Texte imprimé, 9783319013220 • Realtime Data Mining, Texte imprimé, 9783319344454 |
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| 100 | 1 | |a Paprotny, Alexander. | |
| 245 | 1 | 0 | |a Realtime data mining : |b self-learning techniques for recommendation angines |c by Alexander Paprotny, Michael Thess. |
| 250 | |a 1st ed. 2013. | ||
| 260 | |a Cham : |b Springer International Publishing. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Applied and Numerical Harmonic Analysis |x 2296-5017 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a 1 Brave New Realtime World Introduction 2 Strange Recommendations? On The Weaknesses Of Current Recommendation Engines 3 Changing Not Just Analyzing Control Theory And Reinforcement Learning 4 Recommendations As A Game Reinforcement Learning For Recommendation Engines 5 How Engines Learn To Generate Recommendations Adaptive Learning Algorithms 6 Up The Down Staircase Hierarchical Reinforcement Learning 7 Breaking Dimensions Adaptive Scoring With Sparse Grids 8 Decomposition In Transition - Adaptive Matrix Factorization 9 Decomposition In Transition Ii - Adaptive Tensor Factorization 10 The Big Picture Towards A Synthesis Of Rl And Adaptive Tensor Factorization 11 What Cannot Be Measured Cannot Be Controlled - Gauging Success With A/B Tests 12 Building A Recommendation Engine The Xelopes Library 13 Last Words Conclusion References Summary Of Notation | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore, it presents promising results of numerous experiments on real-world data. The area of realtime data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today's classic data mining systems. Whereas the latter learn from historical data and then use it to deduce necessary actions, realtime analytics systems learn and act continuously and autonomously. In the vanguard of these new analytics systems are recommendation engines. They are principally found on the Internet, where all information is available in realtime and an immediate feedback is guaranteed. This monograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinking by considering recommendation tasks as control-theoretic problems. Realtime Data Mining: Self-Learning Techniques for Recommendation Engines will also interest application-oriented mathematicians because it consistently combines some of the most promising mathematical areas, namely control theory, multilevel approximation, and tensor factorization | ||
| 776 | 0 | |t Realtime Data Mining |b Texte imprimé |z 9783319013206 | |
| 776 | 0 | |t Realtime Data Mining |b Texte imprimé |z 9783319013220 | |
| 776 | 0 | |t Realtime Data Mining |b Texte imprimé |z 9783319344454 | |
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