matriz_recomendacion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy.linalg import svd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cargar el DataFrame df_user_items desde un archivo CSV\n",
    "df_user_items=pd.read_csv('dataset\\df_user_dummies.csv')\n",
    "# Establecer el DataFrame df_users con 'user_id' como índice\n",
    "df_users=df_user_items.set_index('user_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>video production</th>\n",
       "      <th>accounting</th>\n",
       "      <th>animation &amp;amp; modeling</th>\n",
       "      <th>utilities</th>\n",
       "      <th>software training</th>\n",
       "      <th>simulation</th>\n",
       "      <th>indie</th>\n",
       "      <th>strategy</th>\n",
       "      <th>audio production</th>\n",
       "      <th>...</th>\n",
       "      <th>education</th>\n",
       "      <th>massively multiplayer</th>\n",
       "      <th>action</th>\n",
       "      <th>free to play</th>\n",
       "      <th>racing</th>\n",
       "      <th>adventure</th>\n",
       "      <th>design &amp;amp; illustration</th>\n",
       "      <th>web publishing</th>\n",
       "      <th>sports</th>\n",
       "      <th>early access</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.051724</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>--ace--</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.113636</td>\n",
       "      <td>0.659091</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>--ionex--</td>\n",
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       "      <td>0.391304</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2SV-vuLB-Kg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044118</td>\n",
       "      <td>0.455882</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.029412</td>\n",
       "      <td>0.029412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-404PageNotFound-</td>\n",
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       "      <td>0.248322</td>\n",
       "      <td>0.093960</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id  video production  accounting  animation &amp; modeling  \\\n",
       "0            --000--               0.0         0.0                       0.0   \n",
       "1            --ace--               0.0         0.0                       0.0   \n",
       "2          --ionex--               0.0         0.0                       0.0   \n",
       "3       -2SV-vuLB-Kg               0.0         0.0                       0.0   \n",
       "4  -404PageNotFound-               0.0         0.0                       0.0   \n",
       "\n",
       "   utilities  software training  simulation     indie  strategy  \\\n",
       "0        0.0                0.0    0.086207  0.396552  0.103448   \n",
       "1        0.0                0.0    0.113636  0.659091  0.136364   \n",
       "2        0.0                0.0    0.130435  0.391304  0.086957   \n",
       "3        0.0                0.0    0.044118  0.455882  0.176471   \n",
       "4        0.0                0.0    0.060403  0.248322  0.093960   \n",
       "\n",
       "   audio production  ...  education  massively multiplayer    action  \\\n",
       "0               0.0  ...        0.0               0.172414  0.672414   \n",
       "1               0.0  ...        0.0               0.022727  0.545455   \n",
       "2               0.0  ...        0.0               0.130435  0.695652   \n",
       "3               0.0  ...        0.0               0.014706  0.455882   \n",
       "4               0.0  ...        0.0               0.174497  0.651007   \n",
       "\n",
       "   free to play    racing  adventure  design &amp; illustration  \\\n",
       "0      0.482759  0.000000   0.137931                        0.0   \n",
       "1      0.363636  0.022727   0.250000                        0.0   \n",
       "2      0.434783  0.000000   0.260870                        0.0   \n",
       "3      0.058824  0.029412   0.323529                        0.0   \n",
       "4      0.288591  0.013423   0.187919                        0.0   \n",
       "\n",
       "   web publishing    sports  early access  \n",
       "0             0.0  0.051724      0.051724  \n",
       "1             0.0  0.022727      0.000000  \n",
       "2             0.0  0.000000      0.000000  \n",
       "3             0.0  0.029412      0.029412  \n",
       "4             0.0  0.026846      0.026846  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user_items.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Realizar la descomposición de valores singulares (SVD)\n",
    "U,s,V_transp=svd(df_users, full_matrices=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcular la varianza explicada por cada valor singular y graficarla\n",
    "sigma_s=[]\n",
    "for i in range(len(s)):\n",
    "    a=(s[i]/sum(s))*100\n",
    "    sigma_s.append(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 4))\n",
    "plt.plot([x for x in range(len(sigma_s))], sigma_s)\n",
    "plt.xlabel('Número de Componentes Principales')\n",
    "plt.ylabel('Varianza Explicada (%)')\n",
    "plt.title('Varianza Explicada por Componente Principal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94.6787499053627"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sumar la varianza explicada de los primeros 10 componentes principales para confirmar el porcentaje de informacion que representan\n",
    "total_varianza_explicada = sum(sigma_s[0:10])\n",
    "total_varianza_explicada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seleccionar el número de componentes principales (k)\n",
    "k = 9  # porque representan el 94.6% de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.00461</td>\n",
       "      <td>0.00347</td>\n",
       "      <td>0.00146</td>\n",
       "      <td>-0.00119</td>\n",
       "      <td>-0.00049</td>\n",
       "      <td>-0.00092</td>\n",
       "      <td>0.00339</td>\n",
       "      <td>-0.00207</td>\n",
       "      <td>0.00230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.00460</td>\n",
       "      <td>0.00186</td>\n",
       "      <td>-0.00556</td>\n",
       "      <td>-0.00112</td>\n",
       "      <td>0.00660</td>\n",
       "      <td>0.00395</td>\n",
       "      <td>0.00443</td>\n",
       "      <td>-0.00502</td>\n",
       "      <td>0.00132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.00477</td>\n",
       "      <td>0.00223</td>\n",
       "      <td>0.00060</td>\n",
       "      <td>0.00081</td>\n",
       "      <td>0.00141</td>\n",
       "      <td>0.00140</td>\n",
       "      <td>-0.00054</td>\n",
       "      <td>-0.00292</td>\n",
       "      <td>0.00381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.00342</td>\n",
       "      <td>-0.00282</td>\n",
       "      <td>-0.00449</td>\n",
       "      <td>0.00219</td>\n",
       "      <td>0.00231</td>\n",
       "      <td>0.00415</td>\n",
       "      <td>0.00215</td>\n",
       "      <td>-0.00085</td>\n",
       "      <td>-0.00301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.00391</td>\n",
       "      <td>0.00044</td>\n",
       "      <td>0.00291</td>\n",
       "      <td>0.00073</td>\n",
       "      <td>-0.00169</td>\n",
       "      <td>-0.00061</td>\n",
       "      <td>0.00035</td>\n",
       "      <td>-0.00038</td>\n",
       "      <td>-0.00082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70907</th>\n",
       "      <td>-0.00337</td>\n",
       "      <td>0.00209</td>\n",
       "      <td>-0.00224</td>\n",
       "      <td>0.00521</td>\n",
       "      <td>-0.00700</td>\n",
       "      <td>-0.00907</td>\n",
       "      <td>0.01038</td>\n",
       "      <td>-0.01553</td>\n",
       "      <td>0.00244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70908</th>\n",
       "      <td>-0.00465</td>\n",
       "      <td>0.00939</td>\n",
       "      <td>-0.00180</td>\n",
       "      <td>-0.00078</td>\n",
       "      <td>-0.00453</td>\n",
       "      <td>0.00483</td>\n",
       "      <td>0.00607</td>\n",
       "      <td>0.00250</td>\n",
       "      <td>0.00331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70909</th>\n",
       "      <td>-0.00572</td>\n",
       "      <td>0.00501</td>\n",
       "      <td>0.00045</td>\n",
       "      <td>0.00259</td>\n",
       "      <td>-0.00125</td>\n",
       "      <td>-0.00033</td>\n",
       "      <td>-0.00286</td>\n",
       "      <td>-0.00643</td>\n",
       "      <td>-0.00590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70910</th>\n",
       "      <td>-0.00283</td>\n",
       "      <td>-0.00212</td>\n",
       "      <td>0.00189</td>\n",
       "      <td>-0.00079</td>\n",
       "      <td>0.00101</td>\n",
       "      <td>0.00114</td>\n",
       "      <td>-0.00081</td>\n",
       "      <td>0.00078</td>\n",
       "      <td>0.00048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70911</th>\n",
       "      <td>-0.00223</td>\n",
       "      <td>-0.00062</td>\n",
       "      <td>-0.00124</td>\n",
       "      <td>0.00029</td>\n",
       "      <td>0.00120</td>\n",
       "      <td>0.00234</td>\n",
       "      <td>0.00131</td>\n",
       "      <td>-0.00020</td>\n",
       "      <td>-0.00098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70912 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0        1        2        3        4        5        6        7  \\\n",
       "0     -0.00461  0.00347  0.00146 -0.00119 -0.00049 -0.00092  0.00339 -0.00207   \n",
       "1     -0.00460  0.00186 -0.00556 -0.00112  0.00660  0.00395  0.00443 -0.00502   \n",
       "2     -0.00477  0.00223  0.00060  0.00081  0.00141  0.00140 -0.00054 -0.00292   \n",
       "3     -0.00342 -0.00282 -0.00449  0.00219  0.00231  0.00415  0.00215 -0.00085   \n",
       "4     -0.00391  0.00044  0.00291  0.00073 -0.00169 -0.00061  0.00035 -0.00038   \n",
       "...        ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "70907 -0.00337  0.00209 -0.00224  0.00521 -0.00700 -0.00907  0.01038 -0.01553   \n",
       "70908 -0.00465  0.00939 -0.00180 -0.00078 -0.00453  0.00483  0.00607  0.00250   \n",
       "70909 -0.00572  0.00501  0.00045  0.00259 -0.00125 -0.00033 -0.00286 -0.00643   \n",
       "70910 -0.00283 -0.00212  0.00189 -0.00079  0.00101  0.00114 -0.00081  0.00078   \n",
       "70911 -0.00223 -0.00062 -0.00124  0.00029  0.00120  0.00234  0.00131 -0.00020   \n",
       "\n",
       "             8  \n",
       "0      0.00230  \n",
       "1      0.00132  \n",
       "2      0.00381  \n",
       "3     -0.00301  \n",
       "4     -0.00082  \n",
       "...        ...  \n",
       "70907  0.00244  \n",
       "70908  0.00331  \n",
       "70909 -0.00590  \n",
       "70910  0.00048  \n",
       "70911 -0.00098  \n",
       "\n",
       "[70912 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtener la matriz de usuarios reducida\n",
    "matriz_usuarios=U[:, :k]\n",
    "matriz_usuarios_df=pd.DataFrame(np.round(matriz_usuarios,5))\n",
    "matriz_usuarios_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.21716</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.00039</td>\n",
       "      <td>-0.14601</td>\n",
       "      <td>-0.72072</td>\n",
       "      <td>-0.31528</td>\n",
       "      <td>-0.01853</td>\n",
       "      <td>-0.27082</td>\n",
       "      <td>-0.00146</td>\n",
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       "      <td>-0.02748</td>\n",
       "      <td>-0.03197</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00087</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.00091</td>\n",
       "      <td>-0.00014</td>\n",
       "      <td>-0.00015</td>\n",
       "      <td>-0.03695</td>\n",
       "      <td>0.02476</td>\n",
       "      <td>-0.16801</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>-0.00960</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00013</td>\n",
       "      <td>0.34597</td>\n",
       "      <td>-0.37309</td>\n",
       "      <td>0.81882</td>\n",
       "      <td>-0.01561</td>\n",
       "      <td>-0.14100</td>\n",
       "      <td>0.00059</td>\n",
       "      <td>-0.00019</td>\n",
       "      <td>0.01767</td>\n",
       "      <td>0.01372</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.00077</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.00138</td>\n",
       "      <td>-0.00006</td>\n",
       "      <td>-0.00043</td>\n",
       "      <td>-0.27399</td>\n",
       "      <td>-0.66401</td>\n",
       "      <td>-0.21282</td>\n",
       "      <td>-0.00003</td>\n",
       "      <td>-0.13355</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.00056</td>\n",
       "      <td>0.09896</td>\n",
       "      <td>0.55187</td>\n",
       "      <td>0.16255</td>\n",
       "      <td>-0.00244</td>\n",
       "      <td>-0.22628</td>\n",
       "      <td>-0.00177</td>\n",
       "      <td>-0.00086</td>\n",
       "      <td>-0.00875</td>\n",
       "      <td>-0.01470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.00027</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.00030</td>\n",
       "      <td>-0.00061</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>-0.45191</td>\n",
       "      <td>-0.11905</td>\n",
       "      <td>-0.41117</td>\n",
       "      <td>-0.00012</td>\n",
       "      <td>0.41650</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.00017</td>\n",
       "      <td>0.06586</td>\n",
       "      <td>-0.11784</td>\n",
       "      <td>-0.08955</td>\n",
       "      <td>-0.03119</td>\n",
       "      <td>0.62796</td>\n",
       "      <td>0.00008</td>\n",
       "      <td>0.00010</td>\n",
       "      <td>-0.04599</td>\n",
       "      <td>-0.02749</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00098</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.00068</td>\n",
       "      <td>0.00032</td>\n",
       "      <td>-0.00010</td>\n",
       "      <td>-0.08874</td>\n",
       "      <td>0.31100</td>\n",
       "      <td>-0.54936</td>\n",
       "      <td>0.00011</td>\n",
       "      <td>-0.63395</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.00018</td>\n",
       "      <td>-0.34561</td>\n",
       "      <td>0.11483</td>\n",
       "      <td>0.04008</td>\n",
       "      <td>0.01156</td>\n",
       "      <td>0.07761</td>\n",
       "      <td>-0.00002</td>\n",
       "      <td>-0.00049</td>\n",
       "      <td>0.02656</td>\n",
       "      <td>-0.03024</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.00014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00037</td>\n",
       "      <td>0.00075</td>\n",
       "      <td>0.00014</td>\n",
       "      <td>-0.27475</td>\n",
       "      <td>-0.26918</td>\n",
       "      <td>0.61630</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>-0.31751</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00026</td>\n",
       "      <td>-0.19786</td>\n",
       "      <td>-0.04393</td>\n",
       "      <td>0.15934</td>\n",
       "      <td>0.00122</td>\n",
       "      <td>0.31691</td>\n",
       "      <td>0.00058</td>\n",
       "      <td>0.00033</td>\n",
       "      <td>0.05529</td>\n",
       "      <td>-0.03599</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.00185</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.00139</td>\n",
       "      <td>-0.00178</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>-0.73117</td>\n",
       "      <td>0.38737</td>\n",
       "      <td>0.15676</td>\n",
       "      <td>-0.00022</td>\n",
       "      <td>0.19367</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00014</td>\n",
       "      <td>-0.20392</td>\n",
       "      <td>0.03164</td>\n",
       "      <td>0.02837</td>\n",
       "      <td>-0.05393</td>\n",
       "      <td>-0.44170</td>\n",
       "      <td>-0.00051</td>\n",
       "      <td>0.00032</td>\n",
       "      <td>-0.05634</td>\n",
       "      <td>-0.05379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.00013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.00039</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>-0.00029</td>\n",
       "      <td>-0.11342</td>\n",
       "      <td>0.10875</td>\n",
       "      <td>0.16324</td>\n",
       "      <td>0.00023</td>\n",
       "      <td>-0.29680</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.00010</td>\n",
       "      <td>-0.02318</td>\n",
       "      <td>-0.07109</td>\n",
       "      <td>0.20416</td>\n",
       "      <td>-0.05445</td>\n",
       "      <td>0.39472</td>\n",
       "      <td>-0.00050</td>\n",
       "      <td>-0.00012</td>\n",
       "      <td>-0.20641</td>\n",
       "      <td>0.02003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.00108</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.00149</td>\n",
       "      <td>-0.00027</td>\n",
       "      <td>-0.00009</td>\n",
       "      <td>0.24776</td>\n",
       "      <td>-0.16817</td>\n",
       "      <td>-0.06396</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0.35979</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00033</td>\n",
       "      <td>-0.77948</td>\n",
       "      <td>-0.01264</td>\n",
       "      <td>0.35047</td>\n",
       "      <td>0.00053</td>\n",
       "      <td>0.00172</td>\n",
       "      <td>0.00215</td>\n",
       "      <td>0.00094</td>\n",
       "      <td>-0.03168</td>\n",
       "      <td>-0.19297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 22 columns</p>\n",
       "</div>"
      ],
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       "        0    1        2        3        4        5        6        7   \\\n",
       "0 -0.00107  0.0 -0.00137 -0.00168 -0.00025 -0.13417 -0.42727 -0.14180   \n",
       "1  0.00087 -0.0  0.00091 -0.00014 -0.00015 -0.03695  0.02476 -0.16801   \n",
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       "3 -0.00027 -0.0 -0.00030 -0.00061  0.00003 -0.45191 -0.11905 -0.41117   \n",
       "4  0.00098 -0.0  0.00068  0.00032 -0.00010 -0.08874  0.31100 -0.54936   \n",
       "5  0.00014  0.0  0.00037  0.00075  0.00014 -0.27475 -0.26918  0.61630   \n",
       "6 -0.00185 -0.0 -0.00139 -0.00178  0.00002 -0.73117  0.38737  0.15676   \n",
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       "8  0.00108 -0.0  0.00149 -0.00027 -0.00009  0.24776 -0.16817 -0.06396   \n",
       "\n",
       "        8        9   ...       12       13       14       15       16  \\\n",
       "0 -0.00016 -0.21716  ... -0.00039 -0.14601 -0.72072 -0.31528 -0.01853   \n",
       "1  0.00005 -0.00960  ...  0.00013  0.34597 -0.37309  0.81882 -0.01561   \n",
       "2 -0.00003 -0.13355  ... -0.00056  0.09896  0.55187  0.16255 -0.00244   \n",
       "3 -0.00012  0.41650  ... -0.00017  0.06586 -0.11784 -0.08955 -0.03119   \n",
       "4  0.00011 -0.63395  ... -0.00018 -0.34561  0.11483  0.04008  0.01156   \n",
       "5  0.00001 -0.31751  ...  0.00026 -0.19786 -0.04393  0.15934  0.00122   \n",
       "6 -0.00022  0.19367  ...  0.00014 -0.20392  0.03164  0.02837 -0.05393   \n",
       "7  0.00023 -0.29680  ... -0.00010 -0.02318 -0.07109  0.20416 -0.05445   \n",
       "8  0.00002  0.35979  ...  0.00033 -0.77948 -0.01264  0.35047  0.00053   \n",
       "\n",
       "        17       18       19       20       21  \n",
       "0 -0.27082 -0.00146 -0.00056 -0.02748 -0.03197  \n",
       "1 -0.14100  0.00059 -0.00019  0.01767  0.01372  \n",
       "2 -0.22628 -0.00177 -0.00086 -0.00875 -0.01470  \n",
       "3  0.62796  0.00008  0.00010 -0.04599 -0.02749  \n",
       "4  0.07761 -0.00002 -0.00049  0.02656 -0.03024  \n",
       "5  0.31691  0.00058  0.00033  0.05529 -0.03599  \n",
       "6 -0.44170 -0.00051  0.00032 -0.05634 -0.05379  \n",
       "7  0.39472 -0.00050 -0.00012 -0.20641  0.02003  \n",
       "8  0.00172  0.00215  0.00094 -0.03168 -0.19297  \n",
       "\n",
       "[9 rows x 22 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtener la matriz de elementos reducida\n",
    "matriz_elementos=V_transp[:k, :]\n",
    "matriz_elementos_df=pd.DataFrame(np.round(matriz_elementos, 5))\n",
    "matriz_elementos_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>video production</th>\n",
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       "      <td>0.00000</td>\n",
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       "      <td>-0.00087</td>\n",
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       "      <td>0.00207</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>-0.00197</td>\n",
       "      <td>0.00308</td>\n",
       "      <td>0.00435</td>\n",
       "      <td>0.00023</td>\n",
       "      <td>0.00099</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00076</td>\n",
       "      <td>-0.00071</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.00001</td>\n",
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       "      <td>0.00269</td>\n",
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       "      <td>0.00001</td>\n",
       "      <td>0.00392</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00042</td>\n",
       "      <td>0.00031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.00000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>-0.00072</td>\n",
       "      <td>-0.00047</td>\n",
       "      <td>0.00016</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00191</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>0.00234</td>\n",
       "      <td>0.00405</td>\n",
       "      <td>0.00148</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0.00017</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>0.00006</td>\n",
       "      <td>0.00027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70907</th>\n",
       "      <td>-0.00002</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>-0.00003</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>-0.00348</td>\n",
       "      <td>0.00454</td>\n",
       "      <td>-0.00434</td>\n",
       "      <td>-0.00001</td>\n",
       "      <td>0.01799</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00189</td>\n",
       "      <td>0.00080</td>\n",
       "      <td>-0.00180</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>-0.00973</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00176</td>\n",
       "      <td>-0.00078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70908</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.00371</td>\n",
       "      <td>0.00287</td>\n",
       "      <td>0.00640</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00043</td>\n",
       "      <td>-0.00181</td>\n",
       "      <td>0.01136</td>\n",
       "      <td>-0.00054</td>\n",
       "      <td>-0.00066</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>-0.00047</td>\n",
       "      <td>-0.00063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70909</th>\n",
       "      <td>0.00001</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00085</td>\n",
       "      <td>0.00085</td>\n",
       "      <td>-0.00183</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>0.00234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00861</td>\n",
       "      <td>0.00251</td>\n",
       "      <td>0.00218</td>\n",
       "      <td>0.00043</td>\n",
       "      <td>0.00088</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>0.00175</td>\n",
       "      <td>0.00139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70910</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>0.00051</td>\n",
       "      <td>-0.00031</td>\n",
       "      <td>0.00080</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.00117</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.00099</td>\n",
       "      <td>0.00394</td>\n",
       "      <td>0.00006</td>\n",
       "      <td>0.00012</td>\n",
       "      <td>0.00124</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>-0.00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70911</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.00140</td>\n",
       "      <td>0.00212</td>\n",
       "      <td>0.00159</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.00076</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.00037</td>\n",
       "      <td>0.00122</td>\n",
       "      <td>0.00004</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00133</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00021</td>\n",
       "      <td>0.00007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70912 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       video production  accounting  animation &amp; modeling  utilities  \\\n",
       "0               0.00000        -0.0                   0.00001    0.00000   \n",
       "1               0.00001        -0.0                   0.00002    0.00001   \n",
       "2               0.00001        -0.0                   0.00002    0.00001   \n",
       "3              -0.00000        -0.0                   0.00000    0.00001   \n",
       "4              -0.00000        -0.0                  -0.00000    0.00000   \n",
       "...                 ...         ...                       ...        ...   \n",
       "70907          -0.00002        -0.0                  -0.00000   -0.00003   \n",
       "70908           0.00000        -0.0                   0.00001   -0.00000   \n",
       "70909           0.00001        -0.0                   0.00001    0.00001   \n",
       "70910           0.00000        -0.0                   0.00000    0.00001   \n",
       "70911           0.00000        -0.0                   0.00000    0.00000   \n",
       "\n",
       "       software training  simulation    indie  strategy  audio production  \\\n",
       "0                0.00000    -0.00075  0.00202   0.00000          -0.00000   \n",
       "1                0.00000    -0.00144  0.00778   0.00058          -0.00000   \n",
       "2                0.00000     0.00118  0.00049  -0.00087           0.00000   \n",
       "3                0.00000    -0.00277  0.00496   0.00269          -0.00000   \n",
       "4               -0.00000    -0.00072 -0.00047   0.00016           0.00000   \n",
       "...                  ...         ...      ...       ...               ...   \n",
       "70907            0.00001    -0.00348  0.00454  -0.00434          -0.00001   \n",
       "70908            0.00000    -0.00371  0.00287   0.00640           0.00000   \n",
       "70909            0.00000     0.00085  0.00085  -0.00183          -0.00000   \n",
       "70910           -0.00000     0.00051 -0.00031   0.00080           0.00000   \n",
       "70911            0.00000    -0.00140  0.00212   0.00159           0.00000   \n",
       "\n",
       "           rpg  ...  education  massively multiplayer   action  free to play  \\\n",
       "0      0.00298  ...    0.00000               -0.00014  0.00319       0.00496   \n",
       "1     -0.00135  ...    0.00001               -0.00419  0.00075       0.00262   \n",
       "2      0.00207  ...    0.00000               -0.00197  0.00308       0.00435   \n",
       "3     -0.00091  ...    0.00000               -0.00047  0.00103      -0.00257   \n",
       "4      0.00191  ...   -0.00000                0.00234  0.00405       0.00148   \n",
       "...        ...  ...        ...                    ...      ...           ...   \n",
       "70907  0.01799  ...    0.00000                0.00189  0.00080      -0.00180   \n",
       "70908  0.00380  ...    0.00001                0.00043 -0.00181       0.01136   \n",
       "70909  0.00234  ...    0.00000                0.00861  0.00251       0.00218   \n",
       "70910 -0.00117  ...    0.00000               -0.00099  0.00394       0.00006   \n",
       "70911 -0.00076  ...    0.00000               -0.00037  0.00122       0.00004   \n",
       "\n",
       "        racing  adventure  design &amp; illustration  web publishing   sports  \\\n",
       "0     -0.00001   -0.00295                    0.00001         0.00000  0.00033   \n",
       "1      0.00022   -0.00063                    0.00002         0.00001  0.00140   \n",
       "2      0.00023    0.00099                    0.00002         0.00001  0.00076   \n",
       "3      0.00001    0.00392                    0.00001         0.00000  0.00042   \n",
       "4      0.00002    0.00017                   -0.00000        -0.00000  0.00006   \n",
       "...        ...        ...                        ...             ...      ...   \n",
       "70907  0.00007   -0.00973                    0.00001         0.00001  0.00176   \n",
       "70908 -0.00054   -0.00066                    0.00002         0.00001 -0.00047   \n",
       "70909  0.00043    0.00088                    0.00000        -0.00000  0.00175   \n",
       "70910  0.00012    0.00124                    0.00000         0.00000  0.00002   \n",
       "70911  0.00000    0.00133                    0.00000         0.00000  0.00021   \n",
       "\n",
       "       early access  \n",
       "0          -0.00041  \n",
       "1          -0.00065  \n",
       "2          -0.00071  \n",
       "3           0.00031  \n",
       "4           0.00027  \n",
       "...             ...  \n",
       "70907      -0.00078  \n",
       "70908      -0.00063  \n",
       "70909       0.00139  \n",
       "70910      -0.00005  \n",
       "70911       0.00007  \n",
       "\n",
       "[70912 rows x 22 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Realizar predicciones de afinidad\n",
    "gustos_predichos=np.dot(matriz_usuarios, matriz_elementos)\n",
    "gustos_predichos_df=pd.DataFrame(np.round(gustos_predichos,5), columns=df_users.columns)\n",
    "gustos_predichos_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenar 'user_id' y las predicciones de afinidad en un nuevo DataFrame\n",
    "gustos_predichos_df=pd.concat([df_user_items[['user_id']],gustos_predichos_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Guardado del DataFrame resultante en un archivo CSV\n",
    "gustos_predichos_df.to_csv('Matriz_recomendacios.csv', index=False)"
   ]
  }
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