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26197588
Recommender Systems: The Textbook 1st ed. 2016 Edition
85% of respondents would recommend this to a friend
Item #:
26197588
SAR 316
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What Stands Out
Comprehensive Coverage
This textbook offers in-depth insights into various recommender system methodologies, making it an essential resource for both students and professionals seeking to understand the complexities of recommendation algorithms.
Practical Examples
Real-world case studies and practical examples are included, illustrating theoretical concepts in action. This feature enhances comprehension and enables readers to apply their knowledge effectively in practical scenarios.
Updated Research
With the latest research and advancements in the field, this edition provides contemporary insights that keep readers informed about emerging trends and future directions in recommender systems.
Product Details
| Publisher | Springer |
| Publication date | April 4, 2016 |
| Edition | 1st ed. 2016 |
| Language | English |
| Print length | 519 pages |
| ISBN-10 | 3319296574 |
| ISBN-13 | 978-3319296579 |
| Item Weight | 3.75 pounds (1.7 kg) |
| Dimensions | 7.3 x 1.3 x 10.1 inches (18.5 x 3.3 x 25.7 cm) |
Who Should Buy?
-
Students
Ideal for students studying data science or machine learning, as it provides foundational concepts and practical applications of recommender systems.
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Researchers
Researchers in the field can benefit from this textbook, offering in-depth analysis and citations for academic work in recommender systems.
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Data Scientists
Practicing data scientists looking to enhance their understanding of algorithms can find valuable insights and techniques within this textbook.
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Beginners
Individuals with no prior experience in data science may find the content too advanced and challenging to comprehend fully.
Product Description
Recommender Systems: The Textbook 1st ed. 2016 Edition
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Intelligence & Semantics Editorial Review
The "Recommender Systems: The Textbook 1st ed. 2016 Edition" is an excellent and comprehensive resource for the intermediate study of the taxonomy of recommender systems. The content is well-organized, and the Pros and Cons of each system are weighed appropriately. The text gives ample discussion on common pitfalls and enlightens on essential algorithms and mathematics that power recommendation engines. Customers appreciate that the author points out subtle details that are helpful in building recommendation systems. The book has given some customers enough insight to build sophisticated recommendation engines. It is not recommended for those looking for a state-of-the-art recommendation system, but it is an excellent reference for building custom recommendation engines. The writing is clear and accessible, and the book does not get into specific languages and frameworks.
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Pros
- Comprehensive and accessible resource for intermediate study
- Well-organized content
- Weighs Pros and Cons of each system appropriately
- Enlightens on essential algorithms and mathematics that power recommendation engines
- Clear and accessible writing
- Helpful for building custom recommendation engines
Cons
- Not recommended for those seeking state-of-the-art recommendation systems
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