Recommender
- Web Process: Setup up Django to collect user's interest and provide recommendations once available.
- Machine Learning Pipeline: Extract data from Django, transform it, and train a Collaborative Filtering model.
- Worker Process: This is the glue. We'll use Celery to schedule/run the trained model predictions and update data for Django-related user recommendations.
- Python 3.6+ (such as 30 Days of Python)
- Django 3.2+ (such as Your First Django Web Project or Try Django 3.2)
- Celery with Django (such as Time & Tasks 2 or this blog post)
Sections
Welcome
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Walkthrough & Requirements
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Where to get help
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Setup Project
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Django as a ML Pipeline Orchestration Tool
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Generate Fake User Data
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Django Management Command to add Fake User Data
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Our Collaborative Filtering Dataset
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Load The Movies Dataset into the Movie Django Model
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Create Ratings Model with Generic Foreign Keys
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Calculate Average Ratings
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Generate Movie Ratings
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Handling Duplicate Ratings with Signals
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Calculate Movie Average Rating Task
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Setup Celery for Offloading Tasks
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Converting Functions into Celery Tasks
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Movie List & Detail View, URLs and Templates
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Django AllAuth
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Update the Movie Ratings Task
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Rendering Rating Choices
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Dislay a User's Ratings
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Dynamic Requests with HTMX
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Rate Movies Dynamically with HTMX
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Infinite Rating Flow with Django & HTMX
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Rating Dataset Exports Model & Task
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Using Jupyter with Django
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Load Real Ratings to Fake Users
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Update Movie Data
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Recommendations by Popularity
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What is Collaborative Filtering
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Collaborative Filtering with Surprise ML
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Surprise ML Utils & Celery Task For Surprise Model Training
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Batch User Prediction Task
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Storing Predictions in our Suggestion Model
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Updating Batch Predictions Based on Previous Suggestions
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ML-Based Movies Recommendations View
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Trigger ML Predictions Per User Activity
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Position Ranking for Movie Querysets
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Movie Embedding Idx Field and Task
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Movie Dataset Exports
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Schedule for ML Training, ML Inference, Movie IDX Updates, and Exports
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Overview of a Neural Network Colab Filtering Model
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Thank you and next steps
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