Probabilistic approaches to recommendations /
The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging t...
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| Main Authors: | , , |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
[2014]
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| Series: | Synthesis lectures on data mining and knowledge discovery ;
#9. |
| Subjects: | |
| Online Access: | Connect to this title online |
| Summary: | The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging task: real-world scenarios involve users behaving in complex situations, where prior beliefs, specific tendencies, and reciprocal influences jointly contribute to determining the preferences of users toward huge amounts of information, services, and products. Probabilistic modeling represents a robust formal mathematical framework to model these assumptions and study their effects in the recommendation process. |
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| Physical Description: | 1 online resource (xv, 181 pages) : illustrations. |
| Bibliography: | Includes bibliographical references (pages 161-179). |
| ISBN: | 9781627052580 1627052585 9783031019067 3031019067 |
| ISSN: | 2151-0075 ; |