Ministry of digital technologies of the republic of uzbekistan

MINISTRY OF DIGITAL TECHNOLOGIES OF THE REPUBLIC OF UZBEKISTAN


TASHKENT UNIVERSITY OF INFORMATION TECHNOLOGIES NAMED AFTER MUKHAMMAD
AL-KHOREZMI

Theme: Models and algorithms for recommending objects based on profile data of corporate network users

Student: Toshniyozov O
Teacher: Qo’chqarov M

Tashkent 2023

Plan:

1. 
   ABSTRACT
   
2. 
   INTRODUCTION
   
   1. 
      What’s recommendation system
      
   2. 
      How Do Recommender Systems Work?
      
3. 
   RECOMMENDATION SYSTEM ALGORITHMS.
   
   1. 
      Benefits of using Recommendation systems.
      
   2. 
      Types of Recommendation systems
      
   3. 
      Step-by-step process to build a recommendation system Using Machine Learning
      
4. 
   CONCLUSION
   
5. 
   List of used sources
   

ABSTRACT
Nowadays, Global customer data generation is increasing at unprecedented rate. Companies are using AI and Machine learning to make use of this data in inventive ways. An ML-powered recommendation system can use customer information effectively to personalize user experience, increase engagement and retention, and finally drive greater sales.
As an example, in 2021, Neftlix detailed that its recommendation system helped grow revenue by $1 billion per year. Amazon is another company that benefits from supplying personalized recommendations to its customer. In 2021, Amazon reported that its recommendation system helped increase sales by 35%.
INTRODUCTION
2.1.What’s a Recommendation System?
A recommendation system is an algorithm that utilizes data analysis and machine learning techniques to propose relevant information (movies, videos, items) to users that it may seem to be interesting for them.
These systems analyze large amounts of data about users’ past behavior, preferences, and interests utilizing machine learning algorithms like clustering, collaborative filtering, and deep neural networks to generate personalized recommendations.
Netflix, Amazon, and Spotify are well-known examples of robust recommendation systems. Netflix gives personalized movie suggestions, Amazon suggests products based on past purchases and browsing history, and Spotify provides personalized playlists and song suggestions based on listening history and preferences.
Recommendation engines provide a personalized user experience, by helping every single consumer identify and discover their favorite movies, TV shows, digital products, books, articles, services, and more. These systems help businesses increase sales and benefit consumers. Amazon lists millions of products on its website; users will likely face issues navigating and finding which products to buy. With Recommendation Systems, consumers can easily find products, promote ease of use, and compel consumers to continue using the site versus navigating away.