How we Used NLP and Django to Build a...
summary
Built a working movie recommendation engine in two days using Python, Django, NLTK and scikit learn, scraping 436 CIFF film records into Django models, running TF‑IDF and Word Sense Disambiguation comparisons (≈189,000 pairwise checks) plus VADER sentiment scores, and deploying a cron‑driven Twitterbot to tweet showtime suggestions. Code uses urllib3/BeautifulSoup for scraping, Django management commands for jobs, and the system performs well enough for festival recommendations.
AI-written summary · about
chapters (8)
- 0:00Start
- 1:49About the Project
- 4:28First Things First
- 5:47Scraping the Data
- 9:50Django Management Command
- 12:09Natural Language Processing
- 18:45NLTK and sck
- 31:49Schedule Tweets with Cron
related talks
46mBeyond Bag of Words A Practitioner’s Guide to Advanced NLP
01h 14mBuilding a Recommendation System with a Hybrid Search From Scratch
19mEvaluating narrative-driven movie recommendations on Reddit
33mPicking What to Watch Next: Build a Recommendation System
57mHands-On with LLM-Powered Recommenders: Hybrid Architectures
01h 21mUnlocking Personalization: A Deep Dive into Modern Recommendation Algorithms
01h 31mHands on - Build a Recommender system
28mHere Come The Robots - Django and Machine Learning