chengstone/movie_recommender resource
MovieLens based recommender system.使用MovieLens数据集训练的电影推荐系统。 observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3182
- days_rel: n/a
- days_push: 2713
- n_releases_24m: 0
Adoption not part of the score
1310 stars · 474 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A Jupyter Notebook-based movie recommender system built with TensorFlow 1.0 and Python 3.5, trained on the MovieLens dataset using a text CNN. It predicts ratings and recommends movies by genre, user favorites, and similar-viewer behavior.
Use cases
- build a movie recommendation system with movielens
- learn recommender systems with tensorflow
- predict user movie ratings with a neural network
- recommend similar movies by genre
- implement text cnn for recommendations
- find what users with similar tastes watched
When to choose
- learning how neural recommender systems work end to end
- studying a text CNN applied to recommendation tasks
- following a tutorial-style notebook with detailed Chinese explanations
When to avoid
- production recommendation systems needing modern maintained libraries
- projects requiring recent TensorFlow versions
- scalable distributed recommendation pipelines
Facets
learning-resource · maturity maintenance
machine-learning data-science machine-learning data-science education python recommender-system tensorflow movielens text-cnn jupyter-notebook collaborative-filtering
1 source
- readme: https://github.com/chengstone/movie_recommender · fetched 2026-08-28 · 0def62c013ea
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| chengstone/movie_recommender | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="chengstone/movie_recommender")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem