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movierecommend's Introduction

电影推荐

本项目主要做如下几件事情:

1.使用不同的推荐算法进行用户电影推荐,包括但不限于:

  • UserCF
  • ItemCF
  • Logistic Regression

2.技术栈

  • 推荐算法【最重要】
  • Django【尚未实现】
  • git【一直在用】

3.文件夹构成:

MovieRecommend
|--- __init__.py => 程序入口【暂未开启】
|
|
|----algorithms
|    是常用算法包。在其中会实现常用的算法如UserCF, ItemCF, Logistic Regression 等  
|    |---logisticRegression.py=> 用于实现逻辑回归算法
|    |---similarity.py        => 用于计算相似度的算法实现
|    |---recommend.py         => UserCF/ItemCF 算法实现
|
|----business
|    |----movie.py            => 电影信息数据
|    |----user.py             => 用户信息数据
|
|
|----data
|     data是资源包。本工程使用的电影评分数据就是放在这里,其下载路径是:http://files.grouplens.org/datasets/movielens
|
|----demo
|    算法示例,测试包
|
|
|----tools
     是工具包。用于读取数据,写入训练好的模型等;    

4.欢迎大家积极讨论,有问题可以直接提出Issues

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movierecommend's Issues

关于逻辑回归算法里用户的职业这一特征

if occ in occupation2Id.keys(): # 如果在其中 occ = occupation2Id.get(occ) # else: # 否则,重新生成一个值 occupation2Id[occ] = len(occupation2Id) # 构建一个occupation -> id 的字典 occ = len(occupation2Id) - 1 # 一个新的职业
1、这里为何是 -1 ,而不是直接 occ = len(occupation2Id)
2、电影类型已经ont-hot编码了,职业种类也应该ont-hot编码

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