collaborative filtering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['c:\\\\Users\\\\Farvath\\\\anaconda3\\\\envs\\\\candidate\\\\python312.zip', 'c:\\\\Users\\\\Farvath\\\\anaconda3\\\\envs\\\\candidate\\\\DLLs', 'c:\\\\Users\\\\Farvath\\\\anaconda3\\\\envs\\\\candidate\\\\Lib', 'c:\\\\Users\\\\Farvath\\\\anaconda3\\\\envs\\\\candidate', '', 'c:\\\\Users\\\\Farvath\\\\anaconda3\\\\envs\\\\candidate\\\\Lib\\\\site-packages', 'c:\\\\Users\\\\Farvath\\\\anaconda3\\\\envs\\\\candidate\\\\Lib\\\\site-packages\\\\win32', 'c:\\\\Users\\\\Farvath\\\\anaconda3\\\\envs\\\\candidate\\\\Lib\\\\site-packages\\\\win32\\\\lib', 'c:\\\\Users\\\\Farvath\\\\anaconda3\\\\envs\\\\candidate\\\\Lib\\\\site-packages\\\\Pythonwin']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import sys\n",
    "print(sys.path)\n",
    "import time\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Farvath\\AppData\\Local\\Temp\\ipykernel_11832\\2405757396.py:2: DtypeWarning: Columns (8,12,13,14,15,16,50,51,52,53,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df1=pd.read_csv(\"./data/collaborative filtering/survey_results_public.csv\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                       advertiserurl  \\\n",
      "0  https://www.dice.com/jobs/detail/AUTOMATION-TE...   \n",
      "1  https://www.dice.com/jobs/detail/Information-S...   \n",
      "2  https://www.dice.com/jobs/detail/Business-Solu...   \n",
      "3  https://www.dice.com/jobs/detail/Java-Develope...   \n",
      "4  https://www.dice.com/jobs/detail/DevOps-Engine...   \n",
      "\n",
      "                             company  \\\n",
      "0  Digital Intelligence Systems, LLC   \n",
      "1  University of Chicago/IT Services   \n",
      "2               Galaxy Systems, Inc.   \n",
      "3                      TransTech LLC   \n",
      "4                   Matrix Resources   \n",
      "\n",
      "                            employmenttype_jobstatus  \\\n",
      "0  C2H Corp-To-Corp, C2H Independent, C2H W2, 3 M...   \n",
      "1                                          Full Time   \n",
      "2                                          Full Time   \n",
      "3                                          Full Time   \n",
      "4                                          Full Time   \n",
      "\n",
      "                                      jobdescription               jobid  \\\n",
      "0  Looking for Selenium engineers...must have sol...  Dice Id : 10110693   \n",
      "1  The University of Chicago has a rapidly growin...  Dice Id : 10114469   \n",
      "2  GalaxE.SolutionsEvery day, our solutions affec...  Dice Id : CXGALXYS   \n",
      "3  Java DeveloperFull-time/direct-hireBolingbrook...  Dice Id : 10113627   \n",
      "4  Midtown based high tech firm has an immediate ...  Dice Id : matrixga   \n",
      "\n",
      "  joblocation_address                                           jobtitle  \\\n",
      "0         Atlanta, GA                           AUTOMATION TEST ENGINEER   \n",
      "1         Chicago, IL                      Information Security Engineer   \n",
      "2      Schaumburg, IL                       Business Solutions Architect   \n",
      "3     Bolingbrook, IL  Java Developer (mid level)- FT- GREAT culture,...   \n",
      "4         Atlanta, GA                                    DevOps Engineer   \n",
      "\n",
      "         postdate                                            shift site_name  \\\n",
      "0      1 hour ago  Telecommuting not available|Travel not required       NaN   \n",
      "1      1 week ago  Telecommuting not available|Travel not required       NaN   \n",
      "2     2 weeks ago  Telecommuting not available|Travel not required       NaN   \n",
      "3     2 weeks ago  Telecommuting not available|Travel not required       NaN   \n",
      "4  48 minutes ago  Telecommuting not available|Travel not required       NaN   \n",
      "\n",
      "                                              skills  \\\n",
      "0                                          SEE BELOW   \n",
      "1  linux/unix, network monitoring, incident respo...   \n",
      "2  Enterprise Solutions Architecture, business in...   \n",
      "3                         Please see job description   \n",
      "4  Configuration Management, Developer, Linux, Ma...   \n",
      "\n",
      "                            uniq_id  \n",
      "0  418ff92580b270ef4e7c14f0ddfc36b4  \n",
      "1  8aec88cba08d53da65ab99cf20f6f9d9  \n",
      "2  46baa1f69ac07779274bcd90b85d9a72  \n",
      "3  3941b2f206ae0f900c4fba4ac0b18719  \n",
      "4  45efa1f6bc65acc32bbbb953a1ed13b7  \n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv(\"./data/collaborative filtering/dice_com-job_us_sample.csv\")\n",
    "df1=pd.read_csv(\"./data/collaborative filtering/survey_results_public.csv\")\n",
    "print(df.head())\n",
    "l=list(df[\"company\"])\n",
    "print(type(l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4292\n",
      "Resource 1\n"
     ]
    }
   ],
   "source": [
    "companies=set(l)\n",
    "print(len(companies))\n",
    "#print(companies)\n",
    "companies=list(companies)\n",
    "len(companies)\n",
    "print(companies[1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.956986427307129\n",
      "   Respondent                        company\n",
      "0           1                       Deloitte\n",
      "1           3  Zoom Technical Services, Inc.\n",
      "2           4                 InSource, Inc.\n",
      "3           5         Crescent Solutions Inc\n",
      "4           7              Bond Street Group\n"
     ]
    }
   ],
   "source": [
    "#Assign random companies to each respondent\n",
    "cbf = pd.DataFrame(df1['Respondent'])\n",
    "import time\n",
    "start=time.time()\n",
    "for i in range(98855):\n",
    "    r=random.randint(1,4291)\n",
    "    cbf.loc[i,\"company\"]=l[r]\n",
    "end=time.time()\n",
    "print(end-start)\n",
    "print(cbf.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# saved the data to colabddata.csv\n",
    "cbf.to_csv(\"./data/collaborative filtering/colabdata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#merge all the dataset having skills from user profile.\n",
    "dflang=pd.read_csv(\"./data/collaborative filtering/LanguageWorkedwith.csv\")\n",
    "dfdata=pd.read_csv(\"./data/collaborative filtering/DatabaseWorkedWith.csv\")\n",
    "dfplat=pd.read_csv(\"./data/collaborative filtering/PlatformWorkedWith.csv\")\n",
    "dfframe=pd.read_csv(\"./data/collaborative filtering/FrameworkWorkedWith.csv\")\n",
    "dfdev=pd.read_csv(\"./data/collaborative filtering/DevType.csv\")\n",
    "dfmerge=pd.merge(dflang,dfdata,on=\"Respondent\")\n",
    "dfmerge=pd.merge(dfmerge,dfplat,on=\"Respondent\")\n",
    "dfmerge=pd.merge(dfmerge,dfframe,on=\"Respondent\")\n",
    "dfmerge=pd.merge(dfmerge,dfdev,on=\"Respondent\")\n",
    "\n",
    "# Dataframe is stored separately as userskills.csv\n",
    "dfmerge.to_csv(\"./data/collaborative filtering/userskills.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import array\n",
    "dfuser=pd.read_csv(\"./data/collaborative filtering/userskills.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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       "   Unnamed: 0.1  Unnamed: 0_x  Respondent  Erlang  Ruby  Delphi/Object Pascal  \\\n",
       "0             0             0           1     0.0   0.0                   0.0   \n",
       "1             1             1           3     0.0   0.0                   0.0   \n",
       "2             2             2           4     0.0   0.0                   0.0   \n",
       "3             3             3           5     0.0   0.0                   0.0   \n",
       "4             4             4           7     0.0   0.0                   0.0   \n",
       "\n",
       "   Assembly  CSS  JavaScript  Python  ...  Back-end developer  Student  \\\n",
       "0       0.0  1.0         1.0     1.0  ...                 0.0      0.0   \n",
       "1       0.0  0.0         1.0     1.0  ...                 0.0      0.0   \n",
       "2       0.0  0.0         0.0     0.0  ...                 0.0      0.0   \n",
       "3       0.0  1.0         1.0     0.0  ...                 0.0      0.0   \n",
       "4       0.0  0.0         0.0     0.0  ...                 0.0      1.0   \n",
       "\n",
       "   C-suite executive (CEO, CTO, etc.)  DevOps specialist  Front-end developer  \\\n",
       "0                                 0.0                0.0                  0.0   \n",
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       "\n",
       "   Database administrator  Data scientist or machine learning specialist  \\\n",
       "0                     0.0                                            0.0   \n",
       "1                     1.0                                            0.0   \n",
       "2                     0.0                                            0.0   \n",
       "3                     0.0                                            0.0   \n",
       "4                     0.0                                            0.0   \n",
       "\n",
       "   Embedded applications or devices developer  \\\n",
       "0                                         0.0   \n",
       "1                                         0.0   \n",
       "2                                         0.0   \n",
       "3                                         0.0   \n",
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       "\n",
       "   Desktop or enterprise applications developer  Full-stack developer  \n",
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       "\n",
       "[5 rows x 124 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove duplicate columns during merge\n",
    "dfuser = dfuser.loc[:,~dfuser.columns.duplicated()]\n",
    "dfuser=dfuser.fillna(0)\n",
    "dfuser.shape\n",
    "dfuser.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Respondent', 'Erlang', 'Ruby', 'Delphi/Object Pascal', 'Assembly',\n",
       "       'CSS', 'JavaScript', 'Python', 'Groovy', 'Perl',\n",
       "       ...\n",
       "       'Back-end developer', 'Student', 'C-suite executive (CEO, CTO, etc.)',\n",
       "       'DevOps specialist', 'Front-end developer', 'Database administrator',\n",
       "       'Data scientist or machine learning specialist',\n",
       "       'Embedded applications or devices developer',\n",
       "       'Desktop or enterprise applications developer', 'Full-stack developer'],\n",
       "      dtype='object', length=116)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dfuser = dfuser.drop(columns=['Unnamed: 0.1','Unnamed: 0_x','Unnamed: 0_y','Unnamed: 0_y.1','ESP8266','Drupal','Windows Phone','Unnamed: 0'],axis=1)\n",
    "#dfuser.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove rows with less than 5 skills\n",
    "dfuser = dfuser.loc[(dfuser.sum(axis=1) >= 5), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "m=(np.int32(max(dfuser[\"Respondent\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 3. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 1. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0.\n",
      " 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0.\n",
      " 0. 0. 1.]\n",
      "4.755592107772827\n"
     ]
    }
   ],
   "source": [
    "# Build  a dictionary of respondent id's as keys  and thier skills as values(list)\n",
    "temp=[0]*m\n",
    "vector=np.array(temp)\n",
    "count=1\n",
    "star=time.time()\n",
    "d=dict()\n",
    "for row in dfuser.iterrows():\n",
    "        index,data=row\n",
    "        l=list()\n",
    "        #l=[data.values[0],list(data.values[1:])]\n",
    "        s=np.int32(data.values[0])\n",
    "        d[s]=np.array(list(data.values[1:]))\n",
    "        #print(type(data))\n",
    "end=time.time()\n",
    "print(d[1])\n",
    "print(end-star)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25182509422302246\n"
     ]
    }
   ],
   "source": [
    "#Build the user user similarity matrix based on thier skill vectors\n",
    "#initialise a user user matrix uptill respondent number 5000\n",
    "sim=list()\n",
    "s=time.time()\n",
    "for i in range(5000):\n",
    "        l=list()\n",
    "        l=[0]*5000\n",
    "        sim.append(l)\n",
    "e=time.time()\n",
    "print(e-s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "total time for building similarity matrix:  176.10309195518494\n"
     ]
    }
   ],
   "source": [
    "#count1=1\n",
    "#count2=1\n",
    "t=time.time()\n",
    "for key, value in d.items():\n",
    "        if(key<5000):\n",
    "            print(key)\n",
    "            b=(np.linalg.norm(value))\n",
    "            for key2,value2 in d.items():\n",
    "                if(key2<5000):\n",
    "                    #print(key2)\n",
    "                    a=np.dot(d[key],d[key2])\n",
    "                    ans=a/(np.linalg.norm(value2)*b)\n",
    "                    sim[key][key2]=ans\n",
    "                    #count2+=1\n",
    "            #count1+=1\n",
    "e=time.time()\n",
    "print(\"total time for building similarity matrix: \",e-t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lets try to recommend for user 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Similarity of user 1 with first 10 users [0.7180491222126322, 0.7139328934618736, 0.6825396825396826, 0.729396808625797, 0.7118090427142383, 0.6777017940884184, 0.738634599566731, 0.6956200187410604, 0.7039126159565596, 0.6934303515995477]\n",
      "user 1 is most similar to users id  8\n",
      "with similarity score of  0.738634599566731\n"
     ]
    }
   ],
   "source": [
    "print(\"Similarity of user 1 with first 10 users\",sim[1][2:12])\n",
    "print(\"user 1 is most similar to users id \",sim[1].index(max(sim[1][2:])))\n",
    "# the similarity of user 1 with others \n",
    "print(\"with similarity score of \",(max(sim[1][2:])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Respondent 2 is working in \n",
      "['Chenoa Information Services']\n",
      "Respondent  11 is working in\n",
      "['NORTHROP GRUMMAN']\n"
     ]
    }
   ],
   "source": [
    "dfjob=pd.read_csv(\"./data/collaborative filtering/colabdata.csv\")\n",
    "\n",
    "target_user=2\n",
    "\n",
    "print(f\"Respondent {target_user} is working in \")\n",
    "print(dfjob.loc[dfjob.Respondent==target_user][\"company\"].values)\n",
    "\n",
    "m1=max(sim[target_user][:target_user])\n",
    "m2=max(sim[target_user][target_user+1:])\n",
    "ma=max(m1,m2)\n",
    "suser=sim[target_user].index(ma)\n",
    "\n",
    "print(\"Respondent \",suser,\"is working in\")\n",
    "print(dfjob.loc[dfjob.Respondent==suser][\"company\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "Building the similarity matrix....\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# For buiding  collaborative filtering based on The Content based recommendations for the first 200 Respondents. \n",
    "# I'll be using the exported only for 200 respondents . Be free to change the range\n",
    "dfcont=pd.read_csv(\"./data/collaborative filtering/recommendations.csv\")\n",
    "sim=list()\n",
    "s=time.time()\n",
    "for i in range(200): # set the number of users here \n",
    "        l=list()\n",
    "        l=[0]*200\n",
    "        sim.append(l)\n",
    "e=time.time()\n",
    "print(e-s)\n",
    "t=time.time()\n",
    "print(\"Building the similarity matrix....\\n\")\n",
    "for key, value in d.items():\n",
    "        if(key<200):\n",
    "            #print(key)\n",
    "            b=(np.linalg.norm(value))\n",
    "            for key2,value2 in d.items():\n",
    "                if(key2<200):\n",
    "                    #print(key2)\n",
    "                    a=np.dot(d[key],d[key2])\n",
    "                    ans=a/(np.linalg.norm(value2)*b)\n",
    "                    sim[key][key2]=ans\n",
    "                    #count2+=1\n",
    "            #count1+=1\n",
    "e=time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Respondent 53 was recommended jobs from content based filtering in \n",
      "['Capability IT' 'Robert Half Technology' 'Robert Half Technology'\n",
      " 'RedRiver Systems L.L.C.' 'UST Global Inc' 'CyberCoders' nan\n",
      " 'Beacon Hill Staffing Group, LLC' 'Yash Technologies'\n",
      " 'Resource Logistics']\n",
      "Respondent 53 was recommended job titles from content based filtering \n",
      "['Software Engineer (Python)' 'Backend Web API/PHP Engineer'\n",
      " 'Mid-Level WebAPI/PHP Developer ADDISON $60-80K'\n",
      " 'Full Stack Java Engineer' 'Full Stack Developer'\n",
      " 'Senior Software Engineer - socket.io, Node, C/C++ - $130K DOE'\n",
      " 'Commercial Buildings Software Developer'\n",
      " 'Sr Python Developer and Python Architect' 'Sr. Full Stack Developer'\n",
      " 'Database Admin with Postgre + Cloud experience']\n",
      "\n",
      "\n",
      "Respondent  79 was most similiar to respondent 53\n",
      "\n",
      "Based on respondent  79  the jobs recommended to 53 are \n",
      "['Hub International' 'Vaco - San Francisco'\n",
      " 'AETEA Information Technology Inc' 'iSymmetry' 'Wicresoft NA'\n",
      " 'RedRiver Systems L.L.C.' 'Premier Logic' 'Lithium Technologies, Inc.'\n",
      " 'Strategic Employment Partners' 'CyberCoders']\n",
      "The recommended job titles are \n",
      "['Full Stack Developer' '5 Full Stack Engineers - Long Term Engagements'\n",
      " 'Full Stack Developer'\n",
      " 'JAVA Full Stack Engineer FULL TIME W2 Only and Local To Atlanta'\n",
      " 'Full Stack SDE' 'Full Stack Java Engineer'\n",
      " 'Full Stack Technical Architect' 'Senior Full Stack Engineer'\n",
      " 'C# Full Stack Engineer'\n",
      " 'Full Stack Developer -JavaScript, NODE, Angular -Partial Remote!']\n"
     ]
    }
   ],
   "source": [
    "target_user=53\n",
    "print(f\"Respondent {target_user} was recommended jobs from content based filtering in \")\n",
    "print(dfcont.loc[dfcont.Respondent==target_user][\"company\"].values)\n",
    "print(f\"Respondent {target_user} was recommended job titles from content based filtering \")\n",
    "print(dfcont.loc[dfcont.Respondent==target_user][\"jobtitle\"].values)\n",
    "print(\"\\n\")\n",
    "m1=max(sim[target_user][:target_user])\n",
    "m2=max(sim[target_user][target_user+1:])\n",
    "ma=max(m1,m2)\n",
    "suser=sim[target_user].index(ma)\n",
    "\n",
    "print(\"Respondent \",suser,f\"was most similiar to respondent {target_user}\\n\")\n",
    "print(\"Based on respondent \",suser,f\" the jobs recommended to {target_user} are \")\n",
    "print(dfcont.loc[dfcont.Respondent==suser][\"company\"].values)\n",
    "print(f\"The recommended job titles are \")\n",
    "print(dfcont.loc[dfcont.Respondent==suser][\"jobtitle\"].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# From the above we can see that jobs recommended to each other quite similar depending on the candidate skills and job description skills."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Similarity matrix exported to similarity_matrix.pkl\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "\n",
    "with open('similarity_matrix.pkl', 'wb') as f:\n",
    "  pickle.dump(sim, f)\n",
    "\n",
    "print(\"Similarity matrix exported to similarity_matrix.pkl\")\n"
   ]
  }
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