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1195 lines
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1195 lines
32 KiB
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<!DOCTYPE HTML>
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<title>10.6:实战案例 · GitBook</title>
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<link rel="prev" href="动手实现基于矩阵分解的协同过滤.html" />
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</head>
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<body>
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<li class="chapter " data-level="1.1" data-path="../">
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<a href="../">
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前言
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</a>
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</li>
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<li class="chapter " data-level="1.2" data-path="../Chapter1/">
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<a href="../Chapter1/">
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第一章 绪论
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.2.1" data-path="../Chapter1/为什么要数据挖掘.html">
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<a href="../Chapter1/为什么要数据挖掘.html">
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1.1:为什么要数据挖掘
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</a>
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</li>
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<li class="chapter " data-level="1.2.2" data-path="../Chapter1/什么是数据挖掘.html">
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<a href="../Chapter1/什么是数据挖掘.html">
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1.2: 什么是数据挖掘
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</a>
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</li>
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<li class="chapter " data-level="1.2.3" data-path="../Chapter1/数据挖掘主要任务.html">
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<a href="../Chapter1/数据挖掘主要任务.html">
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1.3:数据挖掘主要任务
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.3" data-path="../Chapter2/">
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<a href="../Chapter2/">
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第二章 数据探索
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.3.1" data-path="../Chapter2/数据与属性.html">
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<a href="../Chapter2/数据与属性.html">
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2.1:数据与属性
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</a>
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</li>
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<li class="chapter " data-level="1.3.2" data-path="../Chapter2/数据的基本统计指标.html">
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<a href="../Chapter2/数据的基本统计指标.html">
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2.2:数据的基本统计指标
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</a>
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</li>
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<li class="chapter " data-level="1.3.3" data-path="../Chapter2/数据可视化.html">
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<a href="../Chapter2/数据可视化.html">
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2.3:数据可视化
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</a>
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</li>
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<li class="chapter " data-level="1.3.4" data-path="../Chapter2/相似性度量.html">
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<a href="../Chapter2/相似性度量.html">
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2.4:相似性度量
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.4" data-path="../Chapter3/">
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<a href="../Chapter3/">
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第三章 数据预处理
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.4.1" data-path="../Chapter3/为什么要数据预处理.html">
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<a href="../Chapter3/为什么要数据预处理.html">
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3.1:为什么要数据预处理
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</a>
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</li>
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<li class="chapter " data-level="1.4.2" data-path="../Chapter3/标准化.html">
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<a href="../Chapter3/标准化.html">
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3.2:标准化
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</a>
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</li>
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<li class="chapter " data-level="1.4.3" data-path="../Chapter3/非线性变换.html">
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<a href="../Chapter3/非线性变换.html">
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3.3:非线性变换
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</a>
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</li>
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<li class="chapter " data-level="1.4.4" data-path="../Chapter3/归一化.html">
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<a href="../Chapter3/归一化.html">
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3.4:归一化
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</a>
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</li>
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<li class="chapter " data-level="1.4.5" data-path="../Chapter3/离散值编码.html">
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<a href="../Chapter3/离散值编码.html">
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3.5:离散值编码
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</a>
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</li>
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<li class="chapter " data-level="1.4.6" data-path="../Chapter3/生成多项式特征.html">
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<a href="../Chapter3/生成多项式特征.html">
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3.6:生成多项式特征
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</a>
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</li>
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<li class="chapter " data-level="1.4.7" data-path="../Chapter3/估算缺失值.html">
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<a href="../Chapter3/估算缺失值.html">
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3.7:估算缺失值
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.5" data-path="../Chapter4/">
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<a href="../Chapter4/">
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第四章 k-近邻
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.5.1" data-path="../Chapter4/k-近邻算法思想.html">
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<a href="../Chapter4/k-近邻算法思想.html">
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4.1:k-近邻算法思想
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</a>
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</li>
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<li class="chapter " data-level="1.5.2" data-path="../Chapter4/k-近邻算法原理.html">
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<a href="../Chapter4/k-近邻算法原理.html">
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4.2:k-近邻算法原理
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</a>
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</li>
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<li class="chapter " data-level="1.5.3" data-path="../Chapter4/k-近邻算法流程.html">
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<a href="../Chapter4/k-近邻算法流程.html">
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4.3:k-近邻算法流程
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</a>
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</li>
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<li class="chapter " data-level="1.5.4" data-path="../Chapter4/动手实现k-近邻.html">
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<a href="../Chapter4/动手实现k-近邻.html">
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4.4:动手实现k-近邻
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</a>
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</li>
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<li class="chapter " data-level="1.5.5" data-path="../Chapter4/实战案例.html">
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<a href="../Chapter4/实战案例.html">
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4.5:实战案例
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.6" data-path="../Chapter5/">
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<a href="../Chapter5/">
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第五章 线性回归
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.6.1" data-path="../Chapter5/线性回归算法思想.html">
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<a href="../Chapter5/线性回归算法思想.html">
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5.1:线性回归算法思想
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</a>
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</li>
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<li class="chapter " data-level="1.6.2" data-path="../Chapter5/线性回归算法原理.html">
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<a href="../Chapter5/线性回归算法原理.html">
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5.2:线性回归算法原理
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</a>
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</li>
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<li class="chapter " data-level="1.6.3" data-path="../Chapter5/线性回归算法流程.html">
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<a href="../Chapter5/线性回归算法流程.html">
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5.3:线性回归算法流程
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</a>
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</li>
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<li class="chapter " data-level="1.6.4" data-path="../Chapter5/动手实现线性回归.html">
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<a href="../Chapter5/动手实现线性回归.html">
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5.4:动手实现线性回归
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</a>
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</li>
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<li class="chapter " data-level="1.6.5" data-path="../Chapter5/实战案例.html">
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<a href="../Chapter5/实战案例.html">
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5.5:实战案例
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.7" data-path="../Chapter6/">
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<a href="../Chapter6/">
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第六章 决策树
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.7.1" data-path="../Chapter6/决策树算法思想.html">
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<a href="../Chapter6/决策树算法思想.html">
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6.1:决策树算法思想
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</a>
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</li>
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<li class="chapter " data-level="1.7.2" data-path="../Chapter6/决策树算法原理.html">
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<a href="../Chapter6/决策树算法原理.html">
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6.2:决策树算法原理
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</a>
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</li>
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<li class="chapter " data-level="1.7.3" data-path="../Chapter6/决策树算法流程.html">
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<a href="../Chapter6/决策树算法流程.html">
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6.3:决策树算法流程
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</a>
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</li>
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<li class="chapter " data-level="1.7.4" data-path="../Chapter6/动手实现决策树.html">
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<a href="../Chapter6/动手实现决策树.html">
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6.4:动手实现决策树
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</a>
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</li>
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<li class="chapter " data-level="1.7.5" data-path="../Chapter6/实战案例.html">
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<a href="../Chapter6/实战案例.html">
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6.5:实战案例
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.8" data-path="../Chapter7/">
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<a href="../Chapter7/">
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第七章 k-均值
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.8.1" data-path="../Chapter7/k-均值算法思想.html">
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<a href="../Chapter7/k-均值算法思想.html">
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7.1:k-均值算法思想
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</a>
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</li>
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<li class="chapter " data-level="1.8.2" data-path="../Chapter7/k-均值算法原理.html">
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<a href="../Chapter7/k-均值算法原理.html">
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7.2:k-均值算法原理
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</a>
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</li>
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<li class="chapter " data-level="1.8.3" data-path="../Chapter7/k-均值算法流程.html">
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<a href="../Chapter7/k-均值算法流程.html">
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7.3:k-均值算法流程
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</a>
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</li>
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<li class="chapter " data-level="1.8.4" data-path="../Chapter7/动手实现k-均值.html">
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<a href="../Chapter7/动手实现k-均值.html">
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7.4:动手实现k-均值
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</a>
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</li>
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<li class="chapter " data-level="1.8.5" data-path="../Chapter7/实战案例.html">
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<a href="../Chapter7/实战案例.html">
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7.5:实战案例
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.9" data-path="../Chapter8/">
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<a href="../Chapter8/">
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第八章 Apriori
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.9.1" data-path="../Chapter8/Apriori算法思想.html">
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<a href="../Chapter8/Apriori算法思想.html">
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8.1:Apriori算法思想
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</a>
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<li class="chapter " data-level="1.9.2" data-path="../Chapter8/Apriori算法原理.html">
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<a href="../Chapter8/Apriori算法原理.html">
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8.2:Apriori算法原理
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</a>
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<li class="chapter " data-level="1.9.3" data-path="../Chapter8/Apriori算法流程.html">
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<a href="../Chapter8/Apriori算法流程.html">
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8.3:Apriori算法流程
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</a>
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<li class="chapter " data-level="1.9.4" data-path="../Chapter8/动手实现Apriori.html">
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<a href="../Chapter8/动手实现Apriori.html">
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8.4:动手实现Apriori
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</a>
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<li class="chapter " data-level="1.9.5" data-path="../Chapter8/实战案例.html">
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<a href="../Chapter8/实战案例.html">
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8.5:实战案例
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</a>
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<li class="chapter " data-level="1.10" data-path="../Chapter9/">
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<a href="../Chapter9/">
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第九章 PageRank
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.10.1" data-path="../Chapter9/PageRank算法思想.html">
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<a href="../Chapter9/PageRank算法思想.html">
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9.1:PageRank算法思想
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</a>
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</li>
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<li class="chapter " data-level="1.10.2" data-path="../Chapter9/PageRank算法原理.html">
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<a href="../Chapter9/PageRank算法原理.html">
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9.2:PageRank算法原理
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</a>
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</li>
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<li class="chapter " data-level="1.10.3" data-path="../Chapter9/PageRank算法流程.html">
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<a href="../Chapter9/PageRank算法流程.html">
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9.3:PageRank算法流程
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</a>
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</li>
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<li class="chapter " data-level="1.10.4" data-path="../Chapter9/动手实现PageRank.html">
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<a href="../Chapter9/动手实现PageRank.html">
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9.4:动手实现PageRank
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</a>
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</li>
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<li class="chapter " data-level="1.10.5" data-path="../Chapter9/实战案例.html">
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<a href="../Chapter9/实战案例.html">
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9.5:实战案例
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</a>
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</li>
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</ul>
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<li class="chapter " data-level="1.11" data-path="./">
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<a href="./">
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第十章 推荐系统
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.11.1" data-path="推荐系统概述.html">
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<a href="推荐系统概述.html">
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10.1:推荐系统概述
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</a>
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<a href="基于矩阵分解的协同过滤算法思想.html">
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10.2:基于矩阵分解的协同过滤算法思想
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</a>
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</li>
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<li class="chapter " data-level="1.11.3" data-path="基于矩阵分解的协同过滤算法原理.html">
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<a href="基于矩阵分解的协同过滤算法原理.html">
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10.3:基于矩阵分解的协同过滤算法原理
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</a>
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</li>
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<li class="chapter " data-level="1.11.4" data-path="基于矩阵分解的协同过滤算法流程.html">
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<a href="基于矩阵分解的协同过滤算法流程.html">
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10.4:基于矩阵分解的协同过滤算法流程
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</a>
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</li>
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<li class="chapter " data-level="1.11.5" data-path="动手实现基于矩阵分解的协同过滤.html">
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<a href="动手实现基于矩阵分解的协同过滤.html">
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10.5:动手实现基于矩阵分解的协同过滤
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</a>
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<li class="chapter active" data-level="1.11.6" data-path="实战案例.html">
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<a href="实战案例.html">
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10.6:实战案例
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</a>
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<!-- Title -->
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<a href=".." >10.6:实战案例</a>
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<h1 id="106实战案例">10.6:实战案例</h1>
|
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<h2 id="电影评分数据">电影评分数据</h2>
|
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<p>本次使用电影评分数据为<code>672</code>个用户对<code>9123</code>部电影的评分记录，部分数据如下：</p>
|
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<table>
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<thead>
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<tr>
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<th>userId</th>
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<th>movieRow</th>
|
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<th>rating</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>1</td>
|
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<td>30</td>
|
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<td>2.5</td>
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</tr>
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<tr>
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<td>7</td>
|
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<td>30</td>
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<td>3</td>
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</tr>
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<tr>
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<td>31</td>
|
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<td>30</td>
|
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<td>4</td>
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</tr>
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<tr>
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<td>32</td>
|
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<td>30</td>
|
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<td>4</td>
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</tr>
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</tbody>
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</table>
|
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<p>其中：</p>
|
|
<pre><code>userId：用户编号
|
|
movieRow:电影编号
|
|
rating:评分值
|
|
</code></pre><p>如:</p>
|
|
<ul>
|
|
<li>第一行数据表示用户<code>1</code>对电影<code>30</code>评分为<code>2.5</code>分。</li>
|
|
<li>第二行数据表示用户<code>7</code>对电影<code>30</code>评分为<code>3</code>分。</li>
|
|
</ul>
|
|
<p>然后，我们还有电影编号与电影名字对应的数据如下：</p>
|
|
<table>
|
|
<thead>
|
|
<tr>
|
|
<th>movieRow</th>
|
|
<th>title</th>
|
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</tr>
|
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</thead>
|
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<tbody>
|
|
<tr>
|
|
<td>0</td>
|
|
<td>Toy Story (1995)</td>
|
|
</tr>
|
|
<tr>
|
|
<td>1</td>
|
|
<td>Jumanji (1995)</td>
|
|
</tr>
|
|
<tr>
|
|
<td>2</td>
|
|
<td>Grumpier Old Men (1995)</td>
|
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</tr>
|
|
<tr>
|
|
<td>3</td>
|
|
<td>Waiting to Exhale (1995)</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
<p>其中：</p>
|
|
<pre><code>movieRow：电影编号
|
|
title：电影名称
|
|
</code></pre><p><a href="https://pan.baidu.com/s/1kPLXbkGxMXllXBiNg1yCMQ" target="_blank">数据下载连接 提取码：ve3v</a></p>
|
|
<h2 id="构造用户-电影评分矩阵">构造用户-电影评分矩阵</h2>
|
|
<p>大家已经知道，要使用基于矩阵分解的协同过滤算法，首先得有用户与电影评分的矩阵，而我们实际中的数据并不是以这样的形式保存，所以在使用算法前要先构造出用户-电影评分矩阵，<code>python</code>实现代码如下：</p>
|
|
<pre><code class="lang-python"><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
|
|
<span class="hljs-comment">#获取用户数与电影数</span>
|
|
userNo = max(ratings_df[<span class="hljs-string">'userId'</span>])+<span class="hljs-number">1</span>
|
|
movieNo = max(ratings_df[<span class="hljs-string">'movieRow'</span>])+<span class="hljs-number">1</span>
|
|
|
|
<span class="hljs-comment">#创建电影评分表</span>
|
|
rating = np.zeros((userNo,movieNo))
|
|
<span class="hljs-keyword">for</span> index,row <span class="hljs-keyword">in</span> ratings_df.iterrows():
|
|
rating[int(row[<span class="hljs-string">'userId'</span>]),int(row[<span class="hljs-string">'movieRow'</span>])]=row[<span class="hljs-string">'rating'</span>]
|
|
</code></pre>
|
|
<p>构造出表格后，我们就能使用上一关实现的方法来对用户进行电影推荐了：</p>
|
|
<pre><code class="lang-python">recommend(1,1e-4,0.999,20,100,rating)
|
|
>>>
|
|
为用户1推荐的电影为：
|
|
1:Rumble Fish (1983)
|
|
2:Aquamarine (2006)
|
|
3:Stay Alive (2006)
|
|
4:Betrayal, The (Nerakhoon) (2008)
|
|
5:Midnight Express (1978)。
|
|
|
|
recommend(666,1e-4,0.999,20,100,rating)
|
|
>>>
|
|
为用户666推荐的电影为：
|
|
1:Aquamarine (2006)
|
|
2:It's a Boy Girl Thing (2006)
|
|
3:Kill the Messenger (2014)
|
|
4:Onion Field, The (1979)
|
|
5:Wind Rises, The (Kaze tachinu) (2013)。
|
|
|
|
recommend(555,1e-4,0.999,20,100,rating)
|
|
>>>
|
|
为用户555推荐的电影为：
|
|
1:Return from Witch Mountain (1978)
|
|
2:Hitcher, The (2007)
|
|
3:Betrayal, The (Nerakhoon) (2008)
|
|
4:Listen to Me Marlon (2015)
|
|
5:World of Tomorrow (2015)。
|
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|
|
recommend(88,1e-4,0.999,20,100,rating)
|
|
>>>
|
|
为用户88推荐的电影为：
|
|
1:Now, Voyager (1942)
|
|
2:Betrayal, The (Nerakhoon) (2008)
|
|
3:Aquamarine (2006)
|
|
4:Post Grad (2009)
|
|
5:Hitcher, The (2007)
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</code></pre>
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