?? Building a RecSys: My Journey from 0 to First Deep Learning Model
Embarking on my first full-scale project in machine learning, I set out to build a Recommendation System from scratch using PyTorch and Matrix Factorization. Whether it’s recommending a movie, book, or product, RecSys are everywhere—and building one is as complex as it is rewarding! ??
While the principles of this project are applicable to any recommendation system, I chose to build a movie recommendation system for simplicity. It allowed me to focus on mastering the underlying techniques, without overcomplicating the domain.
In this post, I’ll share the journey of designing the system, from data processing to working with PyTorch, embeddings, and deploying it on Hugging Face. I’ll also touch on how Deep Learning and LLMs can further enhance these systems.
?? Understanding Users: More Than Just Math
At the heart of any recommendation system is the need to understand user preferences. The challenge? Predicting what users will love but haven’t yet discovered. For my project, I chose Matrix Factorization—a collaborative filtering method to uncover latent patterns between users and items.
Even though I used movie recommendations as my test case, this approach is general and can apply to recommending any type of content, from products to articles.
?? Tech Stack Behind the Project
This system was powered by a modern tech stack, and PyTorch was the star player:
???? ? Python: My go-to language for machine learning development ??
???? ? PyTorch: A deep learning framework that allowed me to build and train a custom Matrix Factorization model. Its dynamic computation graph was crucial for handling large matrices.
???? ? Pandas: For data manipulation, including cleaning and organizing the datasets ??
???? ? Flask: To serve real-time recommendations via a simple API ??
???? ? Deep Learning Techniques: Embeddings, matrix factorization, mini-batch training, and epochs for iterative model learning.
?? PyTorch & Training: The Role of Epochs
In deep learning, epochs play a vital role in training. An epoch refers to one complete pass through the training data. Using PyTorch, I built a model where I iterated through the dataset for 5 epochs, refining the model’s parameters after each pass.
With every epoch, I observed how the model’s predictions improved by minimizing the error between predicted and actual ratings.
Key aspects of the training process in PyTorch:
???? ? Mini-batch training: Efficiently handled large datasets by breaking them into smaller batches.
???? ? Adam Optimizer: A widely used optimizer that adjusts the learning rate for faster convergence.
???? ? MSE Loss: Mean Squared Error, used to quantify the difference between predicted and actual ratings.
?? Dataset Adventures: Handling Sparse Data
Like many recommendation systems, this project faced the common challenge of sparse data. Most users haven’t rated all the items (movies), meaning the data matrix is mostly empty.
I worked with two datasets:
???? ? Movies Dataset: Contained basic movie details (e.g., movieId, title).
???? ? Ratings Dataset: Contained user ratings (userId, movieId, rating).
Understanding how to work with this sparse data was key to making the system work efficiently, no matter what type of recommendation system you’re building.
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?? Model Architecture: Matrix Factorization & Latent Features
The Matrix Factorization technique condenses users and items into low-dimensional vectors, revealing hidden patterns:
???? ? User Embeddings: Encoded user preferences ??
???? ? Item Embeddings: Encoded movie characteristics ??
?? From Matrix Factorization to Deep Learning
While Matrix Factorization was a great start, Deep Learning offers more sophisticated methods like Neural Collaborative Filtering (NCF), which uses neural networks to model complex, non-linear user-item interactions.
Additional areas:
???? ? Hybrid Models: Combining collaborative filtering with content-based filtering for more comprehensive recommendations.
???? ? Advanced Embeddings: Leveraging techniques like Transformers to capture not just static but evolving preferences.
?? LLMs: The Next Frontier for Recommendations
Large Language Models (LLMs) like GPT or BERT can unlock even more potential in recommendation systems:
???1.? Personalized Recommendations from Text: Analyzing user reviews and comments to extract deeper insights.
???2.? Summarized User Preferences: LLMs can summarize user behavior for even more accurate recommendations.
Pairing LLMs with RAG systems (Retrieval-Augmented Generation) can take recommendations to the next level—providing not only suggestions but explanations for users.
?? Hugging Face & Gradio: Bringing It to Life
One of my favorite parts of the journey was deploying the model on Hugging Face. The Model Hub made sharing the model easy, while Gradio allowed me to build a user-friendly interface for real-time recommendations.
?? Lessons Learned
???1.? Data is key: The structure and quality of data matter just as much as the model itself.
???2.? Simplicity scales: Complex models are powerful, but sometimes simpler methods like Matrix Factorization can be just as effective.
???3.? PyTorch skills: Learning to handle dynamic computation graphs and mini-batch training was crucial for efficient model training.
???4.? LLMs and RAG systems: The future of recommendation systems lies in combining user data with large language models.
???5.? Deployment matters: A great model isn’t enough—it must be accessible and interactive.
?? Wrapping Up
As a new grad, this project was an incredible experience—from building my first deep learning model with PyTorch to deploying it for real-world use. The journey taught me valuable lessons in machine learning, recommendation systems, and model deployment.
Looking forward, I’m excited to explore deep learning, LLMs, and RAG systems to push the boundaries of what recommendation systems can do. Who doesn’t want to discover their next favorite thing, even before they know they want it? ??
Hugging Face : https://huggingface.co/spaces/varunbilluri/movie-recommender
#PyTorch #MachineLearning #RecommendationSystems #RecSys #DeepLearning #Newgrad #LLM #Data
Meta Certified Dev | LeetCode Global Rank - 9500 | Python | DSA
5 个月Interesting!
Business Development Specialist at Datics Solutions LLC
5 个月Amazing project! It's exciting to see how PyTorch and deep learning can take movie recommendation systems to the next level—can't wait to dive into your process!
Multicloud Engineer Trainee | Cognizant
5 个月Hi Varun plz connect