Project Overview: Recommender System with Python
The "Recommender System with Python" project provides a comprehensive overview of various recommendation systems and demonstrates how to implement them using the Python programming language. This project is neatly divided into several components, each focusing on different methods and technologies involved in building recommendation systems. Below is a breakdown of each component:
1. Recommender System Basics
The project starts with an introduction to the fundamental types of recommender systems. These include:
- Content-Based Filtering: This approach recommends items based on the similarity to items that the user has liked in the past.
- Collaborative Filtering: This method suggests items based on the preferences of other users with similar tastes.
2. Content-Based Filtering with Python
This section presents a practical implementation of content-based filtering using Python. By leveraging the movies dataset from Kaggle, the project exemplifies how to create a recommendation engine that suggests movies to users based on their past preferences.
3. Collaborative Filtering with Python
Here, collaborative filtering is implemented with Python, utilizing both the Kaggle movies dataset and the more comprehensive MovieLens dataset. The focus is on the intricacies of user-user and item-item collaborative filtering.
4. Matrix Factorization with Python
This part delves into theory and implementation of Matrix Factorization in recommendation systems. Again using datasets from Kaggle and MovieLens, it illustrates how matrix factorization techniques can enhance a recommender system's performance by discovering latent factors from user-item interactions.
5. Naver News Recommender
In this segment, a recommender system is applied to news data from Naver. Techniques such as Doc2vec are employed to use embedding methods for generating relevant news recommendations.
6. Deep Learning Recommender Systems
Example code is provided demonstrating how to build recommendation systems using deep learning methodologies. By utilizing Keras, the project showcases how neural networks can be trained to make personalized recommendations.
7. Wide & Deep Recommender System
Based on the Wide & Deep paper, this section implements a recommender model that captures both wide (memorization) and deep (generalization) relationships in data. The implementation sacrifices complexity for conceptual clarity, using Keras for model building.
8. Simple Book Recommender System with Keras
A straightforward implementation of a recommendation system using a book dataset from Kaggle is provided. Keras is again used to design a basic model that recommends books based on user data.
9. Recommender System using ChatGPT
Innovatively, this part explores how AI, specifically ChatGPT, can be used to develop a recommendation system. This integration highlights the use of language models in making recommendations.
10. Explainability in Recommender Systems with LLM
Finally, the project explores how Large Language Models (LLM) can be harnessed to enhance the explainability of recommendation systems. Tools like LangChain and GPT-4o are used to build systems that not only recommend but also explain the reasoning behind recommendations.
This expansive project provides both theoretical foundations and practical coding implementations, making it a valuable resource for anyone interested in developing sophisticated recommender systems using Python and modern AI technologies.