akthammomani/MAXELLA-APP-Movies-Tensorflow-Recommenders-TFRS
Build MAXELLA App to recommend Movies using TensorFlow Recommenders (TFRS)
This project helps entertainment platforms, movie streaming services, or content providers build a movie recommendation system. It takes user movie ratings, demographic information, and movie metadata as input to suggest movies a user might enjoy. The output is a list of personalized movie recommendations for individual users. Content strategists, product managers, or data scientists working in entertainment would find this useful.
No commits in the last 6 months.
Use this if you need to create a personalized movie recommendation engine to enhance user experience and engagement on a platform.
Not ideal if your primary goal is to recommend items other than movies or if you require real-time recommendations for extremely high-volume, low-latency scenarios.
Stars
8
Forks
4
Language
Jupyter Notebook
License
—
Category
Last pushed
Nov 17, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/akthammomani/MAXELLA-APP-Movies-Tensorflow-Recommenders-TFRS"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
asif536/Movie-Recommender-System
Basic Movie Recommendation Web Application using user-item collaborative filtering.
victorverma3/Letterboxd-Movie-Recommendations
Generate AI-powered movie recommendations, discover insightful profile statistics, pick movies...
snowch/movie-recommender-demo
This project walks through how you can create recommendations using Apache Spark machine...
kishan0725/AJAX-Movie-Recommendation-System-with-Sentiment-Analysis
A content-based recommender system that recommends movies similar to the movie the user likes...
skotz/cp-user-behavior
Recommendation engine using collaborative filtering and matrix factorization