GavinHacker / Recsys_core
Licence: mit
[电影推荐系统] Based on the movie scoring data set, the movie recommendation system is built with FM and LR as the core(基于爬取的电影评分数据集，构建以FM和LR为核心的电影推荐系统).
Stars: ✭ 245
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推荐系统
基于机器学习方法的电影推荐系统
v0.10
💡QQ讨论群： 641914109
整体介绍
- recsys_ui: 前端技术(html5+JavaScript+jquery+ajax)
- recsys_web: 后端技术(Java+SpringBoot+mysql)
- recsys_spider: 网络爬虫(python+BeautifulSoup)
- recsys_sql: 使用SQL数据处理
- recsys_model: pandas, libFM, sklearn. pandas数据分析和数据清洗，使用libFM,sklearn对模型初步搭建
- recsys_core: 使用pandas, libFM, sklearn完整的数据处理和模型构建、训练、预测、更新的程序
- recsys_etl：ETL 处理爬虫增量数据时使用kettle ETL便捷处理数据
为了能够输出一个可感受的系统，我们采购了阿里云服务器作为数据库服务器和应用服务器，在线上搭建了电影推荐系统的第一版，地址是:
www.technologyx.cn
可以注册，也可以使用已有用户：
| 用户名 | 密码 |
|---|---|
| gavin | 123 |
| gavin2 | 123 |
| wuenda | 123 |
欢迎登录使用感受一下。
设计思路
用简单地方式表述一下设计思路，
1.后端服务recsys_web依赖于系统数据库的推荐表‘recmovie’展示给用户推荐内容
2.用户对电影打分后（暂时没有对点击动作进行响应），后台应用会向mqlog表插入一条数据（消息）。
3.新用户注册，系统会插入mqlog中一条新用户注册消息
4.新电影添加，系统会插入mqlog中一条新电影添加消息
5.推荐模块recsys_core会拉取用户的打分消息，并且并行的做以下操作：
a.增量的更新训练样本
b.快速（因服务器比较卡，目前设定了延时）对用户行为进行基于内容推荐的召回
c.训练样本更新模型
d.使用FM，LR模型对Item based所召回的数据进行精排
e.处理新用户注册消息，监听到用户注册消息后，对该用户的属性初始化(统计值)。
f.处理新电影添加消息，更新基于内容相似度而生成的相似度矩阵
注：
- 由于线上资源匮乏，也不想使系统增加复杂度，所以没有直接使用MQ组件，而是以数据库表作为代替。
- recsys_model属于用notebook进行数据分析和数据处理以及建模的草稿，地址为：https://github.com/GavinHacker/recsys_model
- 其余的所有项目的地址索引为：https://github.com/GavinHacker/technologyx
模型相关的模块介绍
增量的处理用户comment，即增量处理评分模块
这个模块负责监听来自mqlog的消息，如果消息类型是用户的新的comment,则对消息进行拉取，并相应的把新的comment合并到总的训练样本集合，并保存到一个临时目录 然后更新数据库的config表，把最新的样本集合(csv格式)的路径更新上去
运行截图
消息队列的截图
把csv处理为libsvm数据
这个模块负责把最新的csv文件，异步的处理成libSVM格式的数据，以供libFM和LR模型使用，根据系统的性能确定任务的间隔时间
运行截图
基于内容相似度推荐
当监听到用户有新的comment时，该模块将进行基于内容相似度的推荐，并按照电影评分推荐
运行截图
libFM预测
对已有的基于内容推荐召回的电影进行模型预测打分，呈现时按照打分排序
如下图为打分更新
逻辑回归预测
对样本集中的打分做0，1处理，根据正负样本平衡，> 3分为喜欢 即1， <=3 为0 即不喜欢，这样使用逻辑回归做是否喜欢的点击概率预估，根据概率排序
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