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19388561
Practical Recommender Systems
98% of respondents would recommend this to a friend
物品 #:
19388561
HKD 528
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Practical Recommender Systems explains how recommender systems work and shows how to create and apply them for your site.
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What Stands Out
Expertise in Field
Authored by leading experts, this book provides deep insights into recommender systems, ensuring readers gain robust knowledge from established authorities in the field.
Hands-on Approach
Contains practical examples and real-world applications, enabling readers to implement techniques effectively and enhance their skills in building recommender systems.
Comprehensive Coverage
Addresses various algorithms and methodologies, making it a versatile resource for beginners and seasoned professionals seeking to expand their understanding of recommender technologies.
產品詳情
- Learn to build effective online recommender systems
- Includes a free eBook in multiple formats
- Recommender systems are essential for personalized suggestions
- Covers user data collection, popular recommendation algorithms, and real-world examples
- Addresses scaling problems and other challenges for growing sites
- Written by an experienced data scientist
| Publisher | Manning Publications |
| Publication date | February 2, 2019 |
| Edition | First Edition |
| Language | English |
| Print length | 432 pages |
| ISBN-10 | 1617292702 |
| ISBN-13 | 978-1617292705 |
| Item Weight | 1.62 pounds (730 grams) |
| Dimensions | 7.38 x 1.08 x 9.25 inches (18.7 x 2.7 x 23.5 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to deepen their understanding of recommender systems and practical implementations.
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Software Developers
Useful for developers seeking to integrate recommender systems into their applications with applicable techniques and examples.
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Academic Researchers
Great resource for researchers in machine learning focusing on recommender system methodologies and advancements.
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Beginner Learners
Not suitable for absolute beginners who lack foundational knowledge in data science or machine learning.
產品敘述
Practical Recommender Systems
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Internet, Groupware, & Telecommunications Editorial Review
"Practical Recommender Systems" by Kim Falk is a comprehensive and instructional book for designing and building recommender systems. The book provides a complete learning environment/platform, including a large semi-production-level dataset, a mock website, charts, dashboards, and metrics. Each topic is explained clearly both in terms of the logic behind each recommender algorithm and the python code for each listing. However, the book can be somewhat "inaccessible" for people with less familiarity with some of the tech that the author has employed, and the listings that contain the code for each chapter are sometimes convoluted and wrapped up in inherited classes and objects, making replication of the author's results more labor-intensive than it should be. The book's code sometimes seems disjointed and dependent on the underlying tech that it is used to serve it. Overall, "Practical Recommender Systems" is a valuable resource for anyone looking to learn about practical recommender systems and is highly recommended.
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優點
- Provides a comprehensive learning environment/platform for designing and building recommender systems
- Explains each topic clearly in terms of the logic behind each recommender algorithm and the python code for each listing
- Contains a large semi-production-level dataset, a mock website, charts, dashboards, and metrics
- Has "production-ready" code that can be very "borrowable"
- Is a great resource for learning about practical recommender systems
缺點
- Somewhat inaccessible for people with less familiarity with some of the tech that the author has employed
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HKD 528
現在訂購，約可在下列時間收貨： 星期二, 十月 20
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特色和優勢
- Learn to build a recommender system the right way: it can make or break your application!
- Recommender systems make predictions about what users will be most interested in at a particular time.
- The book covers scaling problems and other issues you'll encounter as your site grows.
- Real-world examples in Python.
- Readers need intermediate programming and database skills.
- Explains how recommender systems work and shows how to create and apply them for your site.
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