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Machine Learning Techniques for Text

You're reading from   Machine Learning Techniques for Text Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803242385
Length 448 pages
Edition 1st Edition
Languages
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Author (1):
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Nikos Tsourakis Nikos Tsourakis
Author Profile Icon Nikos Tsourakis
Nikos Tsourakis
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Toc

Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Introducing Machine Learning for Text 2. Chapter 2: Detecting Spam Emails FREE CHAPTER 3. Chapter 3: Classifying Topics of Newsgroup Posts 4. Chapter 4: Extracting Sentiments from Product Reviews 5. Chapter 5: Recommending Music Titles 6. Chapter 6: Teaching Machines to Translate 7. Chapter 7: Summarizing Wikipedia Articles 8. Chapter 8: Detecting Hateful and Offensive Language 9. Chapter 9: Generating Text in Chatbots 10. Chapter 10: Clustering Speech-to-Text Transcriptions 11. Index 12. Other Books You May Enjoy

Understanding recommender systems

In an ever-growing digital world, customers are often overwhelmed by the choices available and need assistance finding what they want. It comes as no surprise that their habits and preferences are valuable assets to overcome this hurdle. Both assist in identifying user needs and permit companies to promote new products and services at the right time and place. Nonetheless, with most of the services being predominately online, having direct access to your customers is challenging. So, what is the solution?

Let’s consider a few standard user inputs to answer this question, such as the number of stars awarded in an Amazon book review. Ratings provide a quality measure for the items in any online store. Similarly, the view count of a YouTube video is an engagement metric that can be used to recommend the same video to others. The number of views is an implicit indicator while rating scores are explicit. In both cases, however, an automatic system...

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