ramiltiteev/bert4rec
Implementation of the BERT4REC for MovieLens Dataset
This project helps movie streaming services and film enthusiasts improve movie recommendations. By analyzing your watched movie history and considering genres, it predicts what films you're most likely to enjoy next. This is useful for anyone looking to personalize movie discovery or enhance a recommendation system.
No commits in the last 6 months.
Use this if you want to generate highly personalized movie recommendations based on viewing history and genre preferences.
Not ideal if you need a recommendation system for items other than movies or if you don't have access to detailed movie viewing history.
Stars
9
Forks
2
Language
Jupyter Notebook
License
—
Category
Last pushed
May 11, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ramiltiteev/bert4rec"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
benfred/implicit
Fast Python Collaborative Filtering for Implicit Feedback Datasets
NicolasHug/Surprise
A Python scikit for building and analyzing recommender systems
MengtingWan/goodreads
code samples for the goodreads datasets
GHamrouni/Recommender
A C library for product recommendations/suggestions using collaborative filtering (CF)
Md-Emon-Hasan/BookSage-AI
Full-stack hybrid book recommendation system combining Collaborative Filtering and Content-Based...