# water8394/flink-recommandSystem-demo

:helicopter::rocket:基于Flink实现的商品实时推荐系统。flink统计商品热度，放入redis缓存，分析日志信息，将画像标签和实时记录放入Hbase。在用户发起推荐请求后，根据用户画像重排序热度榜，并结合协同过滤和标签两个推荐模块为新生成的榜单的每一个产品添加关联产品，最后返回新的用户列表。

Repository: https://github.com/water8394/flink-recommandSystem-demo
Canonical: https://ross.abutalabs.com/products/flink-recommandsystem-demo
Language: Java
License Family: other
Topics: flink, recommander-system, recommand, flink-kafka, flink-examples, flink-hbase, flink-redis
Last push: 2024-02-04T08:18:39+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": 2667, "days_push": 941, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4480, forks 1478 (observed 2026-08-28T04:08:51.128218+00:00)

## What it is
A real-time product recommendation system built on Apache Flink, demonstrating streaming computation of product popularity, user profiles, and collaborative filtering with data stored in Redis and HBase. It includes a web front end for serving recommendations and an admin dashboard showing real-time metrics.

## Use cases
- build a real-time recommendation engine with Flink
- learn how to combine collaborative filtering and tag-based recommendations
- compute real-time product popularity rankings from Kafka logs
- store user browsing history and profiles in HBase via Flink jobs
- serve personalized re-ranked hot product lists to users
- display real-time recommendation metrics on a data dashboard

## When to choose
- you want a reference implementation of a streaming recommendation pipeline in Java/Flink
- you need to learn Flink windowing, state management, and Kafka/HBase/Redis integration
- you are prototyping an e-commerce recommendation system with Docker-based deployment

## When to avoid
- you need a production-grade, maintained recommendation platform with a license and active support
- you want a turnkey recommender library rather than a demo application
- your stack does not include Flink, Kafka, HBase, and Redis

## Facets
- artifact type: application
- maturity: maintenance
- function: streaming, machine-learning, caching, database, data-visualization, etl
- domain: big-data, e-commerce, analytics
- platform: jvm, self-hosted
- tags: flink, recommendation-system, collaborative-filtering, kafka, hbase, redis, real-time-recommendation, user-profiling, demo-project, data-engineering, real-time, docker

## Member repositories
- water8394/flink-recommandSystem-demo (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:51.128218+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 2026-08-29T18:20:33.644444+00:00, confidence not recorded.
  - readme: https://github.com/water8394/flink-recommandSystem-demo (fetched 2026-08-28T04:08:51.128218+00:00, sha cdf8f02a621d)
- Data as of 2026-08-30T08:39:29.467469+00:00.