# chengstone/movie_recommender

MovieLens based recommender system.使用MovieLens数据集训练的电影推荐系统。

Repository: https://github.com/chengstone/movie_recommender
Canonical: https://ross.abutalabs.com/products/movie_recommender
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2019-03-31T02:18:23+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3182, "days_push": 2713, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1310, forks 474 (observed 2026-08-28T04:04:19.948504+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, data-science, education
- platform: python
- tags: recommender-system, tensorflow, movielens, text-cnn, jupyter-notebook, collaborative-filtering

## Member repositories
- chengstone/movie_recommender (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:19.948504+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:49:54.241584+00:00, confidence not recorded.
  - readme: https://github.com/chengstone/movie_recommender (fetched 2026-08-28T04:04:19.948504+00:00, sha 0def62c013ea)
- Data as of 2026-08-30T08:39:29.467469+00:00.