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Building LLM Powered  Applications

You're reading from   Building LLM Powered Applications Create intelligent apps and agents with large language models

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835462317
Length 342 pages
Edition 1st Edition
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Author (1):
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Valentina Alto Valentina Alto
Author Profile Icon Valentina Alto
Valentina Alto
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Table of Contents (16) Chapters Close

Preface 1. Introduction to Large Language Models 2. LLMs for AI-Powered Applications FREE CHAPTER 3. Choosing an LLM for Your Application 4. Prompt Engineering 5. Embedding LLMs within Your Applications 6. Building Conversational Applications 7. Search and Recommendation Engines with LLMs 8. Using LLMs with Structured Data 9. Working with Code 10. Building Multimodal Applications with LLMs 11. Fine-Tuning Large Language Models 12. Responsible AI 13. Emerging Trends and Innovations 14. Other Books You May Enjoy
15. Index

Introduction to recommendation systems

A recommendation system is a computer program that recommends items for users of digital platforms such as e-commerce websites and social networks. It uses large data sets to develop models of users’ likes and interests, and then recommends similar or recommended items to individual users.There are different types of recommendation systems, depending on the methods and data they use. Some of the common types are:

  • Collaborative filtering: This type of recommendation system uses the ratings or feedback of other users who have similar preferences to the target user. It assumes that users who liked similar items in the past will like similar items in the future. For example, if user A and user B both liked movies X and Y, then the algorithm may recommend movie Z to user A if user B also liked it.
  • Collaborative filtering can be further divided into two subtypes: user-based and item-based:
  • User-based collaborative filtering finds similar users...
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