# CodeRayZhang/Movie_Recommend

基于Spark的电影推荐系统，包含爬虫项目、web网站、后台管理系统以及spark推荐系统

Repository: https://github.com/CodeRayZhang/Movie_Recommend
Canonical: https://ross.abutalabs.com/products/movie_recommend
Language: Java
License: MIT
License Family: permissive
Topics: spark-mllib, spark-streaming, ssm-maven, scrapy, scala, hadoop, nginx, hive, mysql
Last push: 2019-04-01T13:10:59+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3059, "days_push": 2711, "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 3007, forks 1051 (observed 2026-08-28T04:07:37.670106+00:00)

## What it is
A full-stack movie recommendation system built on Spark, including a Scrapy crawler, an SSM-based movie website, an admin backend, and a Spark streaming recommendation engine using Hadoop, Kafka, Hive, and MySQL. It serves real-time personalized movie recommendations based on user click and rating events.

## Use cases
- build a movie recommendation website
- learn how to build a spark recommendation system
- real-time recommendations from user click events
- scrape movie data with scrapy
- end-to-end big data recommendation pipeline example
- spark mllib collaborative filtering project

## When to choose
- learning big data and Spark recommendation architectures end to end
- you want a complete reference project covering crawler, web frontend, admin, and recommendation engine
- teaching or studying Spark MLlib and streaming in a realistic system

## When to avoid
- you need a production-ready, actively maintained recommender
- you want a lightweight solution without a Hadoop/Spark cluster
- your stack is not Java/Scala/Python on Linux

## Facets
- artifact type: application
- maturity: maintenance
- function: machine-learning, web-scraping, web-framework, streaming, data-science, search-engine
- domain: machine-learning, big-data, web-development, e-commerce
- platform: python, jvm
- tags: spark-mllib, recommendation-system, collaborative-filtering, scrapy, ssm, kafka, hive, movie-recommendation, big-data-pipeline, data-engineering, linux, web-server, docker

## Member repositories
- CodeRayZhang/Movie_Recommend (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:37.670106+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 2026-08-29T18:47:23.757680+00:00, confidence not recorded.
  - readme: https://github.com/CodeRayZhang/Movie_Recommend (fetched 2026-08-28T04:07:37.670106+00:00, sha ef6481e9437f)
- Data as of 2026-08-30T08:39:29.467469+00:00.