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Building an AI Recommendation System by a Working Engineer | Recommendation Algorithm | Recommender | Recsys

This course covers everything from core recommendation system algorithms to practical implementation. - Content-based filtering - Collaborative filtering and deep learning-based recommendation model implementation - Two-step recommender systems implementation - Hands-on practice using PyTorch/RecBole - Industry know-how and recommendation result visualization

(5.0) 4 reviews

86 learners

Level Basic

Course period Unlimited

  • Jay
Python
Python
Recommendation System
Recommendation System
AI
AI
recommendation
recommendation
recommender-systems
recommender-systems
Python
Python
Recommendation System
Recommendation System
AI
AI
recommendation
recommendation
recommender-systems
recommender-systems

What you will gain after the course

  • You can understand and directly implement the core algorithms of recommendation systems (Content-based filter, Collaborative filter)

  • You will gain the ability to build and evaluate practical recommendation models using PyTorch and RecBole

  • You will have the ability to review and tune recommendation results

Dive into Recommendation Systems
with a Professional AI Engineer


Design and implement a movie recommendation system yourself.


I created this course based on know-how I gained directly through graduate studies and industry experience.
If you want to solidly build recommendation skills that work in real-world practice,
let's learn together through this course!

Building a Deep Learning recommendation model based on the MovieLens dataset
Complete a personalized movie recommendation system from start to finish.

Build Two-step recommender systems and visualize recommendation results
Gain the ability to analyze and tune recommendation results based on real-world industry expertise.

Build an AI Movie Recommendation System
with Industry Engineers

Section 1 - Recommender Systems Overview and Fundamental Understanding

You will understand the concept of recommendation systems, their business value, and how they differ from other machine learning tasks. You will learn about the importance of recommendation systems for resolving information overload and personalization.

Section 2 - Recommendation System Development Environment Setup and Evaluation Metrics

Set up the experimental environment needed for the course and learn various evaluation metrics to measure the performance of recommendation systems. Additionally, gain an overall understanding of the datasets to be used and the recommendation system architecture.

Section 3 - Content-Based Recommendation System (CBF)

You will learn content-based filtering (CBF) techniques that recommend content similar to a user's past viewing history or preferences. You will practice advanced techniques for recommending movies based on text similarity using Sentence Transformers.

Section 4 - Implementing Collaborative Filtering (CF) Models

You will learn the process of building and training a collaborative filtering (CF) based recommendation model using the RecBole library. In particular, you will optimize recommendation performance based on user-item interactions using the LightGCN model.

Section 5 - Building a Two-step Recommendation System

Implement a Two-step recommendation system that combines content-based filtering and collaborative filtering. Learn how to generate more sophisticated recommendation results by fusing personalized recommendations and content similarity recommendations based on the LightGCN model.

Section 6 - Visualizing Recommendation Results with Streamlit

Effectively visualize the results of the recommendation system built using the Streamlit framework. Develop an interactive visualization page that includes movie posters and detailed information through TMDB API integration.

Is the complex recommendation system hard to grasp?

This course was made just for people like you.

✔️ Beginners and university/graduate students who want to learn recommendation systems

  • Those who want to clearly understand the basic principles of recommendation systems (content-based, collaborative filtering)

  • Those who want to create actual recommendation models hands-on using PyTorch and RecBole

  • Those who want to learn how recommendation algorithms work and develop the ability to interpret their results

✔️ Engineers working in the field who want to implement recommendation systems into their services

  • Those who want to learn practical application methods of the latest recommendation algorithms (deep learning-based, two-step)

  • Those who want to learn the know-how of building recommendation systems used in the field

  • Those who want to gain technical insights by building an actual movie recommendation system

✔️ Developers who want to build a career in the recommendation systems field

  • Those who want to grow as a recommendation system expert in the core technology of popular services (Netflix, YouTube, Coupang)

  • Those who want to strengthen their ability to develop models that innovate user experience based on data

  • Those who want to experience the entire process from building a personalized recommendation system to visualizing results


Now explore the world of recommendation systems that once felt complex with confidence, and gain hands-on experience building your own AI movie recommendation system.

Notes Before Taking the Course


Practice Environment

  • Windows OS

  • Python 3.12

  • PyTorch 2.6 and RecBole

  • Recommended specifications: 8GB RAM or more, 10GB or more SSD storage space

Prerequisites and Notes

  • Understanding of basic Python programming syntax is required.

  • Prior knowledge of machine learning or recommendation systems will be helpful for learning.


Learning Materials

  • All code examples used in the lecture will be provided.

  • The practical exercises will be conducted using the MovieLens dataset.


Recommended for
these people

Who is this course right for?

  • An engineer trying to implement a recommendation system

  • College/graduate students who want to become recommendation system engineers

  • Anyone who wants to learn about recommendation systems

Need to know before starting?

  • Basic Python programming skills are required.

  • You must have experience using PyTorch.

Hello
This is

7,218

Learners

214

Reviews

171

Answers

4.9

Rating

5

Courses

I will create development content that anyone can enjoy and learn from.

Experience ✒️

  • Served as a code reviewer for the 6th cohort of the Naver Connect Boostcamp Web Backend (Node.js) program

  • Seoul Business Agency SeSSAC Online IT Content Partner (Full Stack)

  • Conducted the Seoul Business Agency (SBA) SeSSAC Developer Introductory Course (Python, JavaScript)

  • Conducted lectures for the Konkuk University Immersive Programming Course

  • Head of Server Division, Seoul SSAC Rising Programmer

  • Outsourced development of cryptocurrency automated trading program (Qt)

  • Marketing agency landing page development outsourcing (Web)

  • Outsourced development of real-time data processing Windows application (Qt)

    Outsourcing development of an automated cryptocurrency trading program (Qt) Outsourcing development of a marketing agency landing page (Web) Outsourcing development of a real-time data processing Windows application (Qt)

Curriculum

All

18 lectures ∙ (2hr 7min)

Course Materials:

Lecture resources
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4 reviews

5.0

4 reviews

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