sample-cf-item-based.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "84de56a8",
   "metadata": {},
   "source": [
    "# Sample Code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "688675f5",
   "metadata": {},
   "source": [
    "## 基礎建設"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b32226db",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wei/.virtualenvs/py39/lib/python3.9/site-packages/pandas/compat/__init__.py:97: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import gzip, json\n",
    "\n",
    "def parse(path):\n",
    "    g = gzip.open(path, 'rb')\n",
    "    for l in g:\n",
    "        yield json.loads(l)\n",
    "\n",
    "def getDF(path):\n",
    "    i = 0\n",
    "    df = {}\n",
    "    for d in parse(path):\n",
    "        df[i] = d\n",
    "        i += 1\n",
    "    return pd.DataFrame.from_dict(df, orient='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84ff0f9d",
   "metadata": {},
   "source": [
    "## 載入資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9402e9c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget http://deepyeti.ucsd.edu/jianmo/amazon/categoryFilesSmall/All_Beauty.csv\n",
    "!wget http://deepyeti.ucsd.edu/jianmo/amazon/metaFiles2/meta_All_Beauty.json.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84b66cc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = getDF('/content/meta_All_Beauty.json.gz')\n",
    "ratings = pd.read_csv('/content/All_Beauty.csv', names=['asin', 'reviewerID', 'overall', 'unixReviewTime'], header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9fd77203",
   "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>category</th>\n",
       "      <th>tech1</th>\n",
       "      <th>description</th>\n",
       "      <th>fit</th>\n",
       "      <th>title</th>\n",
       "      <th>also_buy</th>\n",
       "      <th>tech2</th>\n",
       "      <th>brand</th>\n",
       "      <th>feature</th>\n",
       "      <th>rank</th>\n",
       "      <th>also_view</th>\n",
       "      <th>details</th>\n",
       "      <th>main_cat</th>\n",
       "      <th>similar_item</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>asin</th>\n",
       "      <th>imageURL</th>\n",
       "      <th>imageURLHighRes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[Loud 'N Clear Personal Sound Amplifier allows...</td>\n",
       "      <td></td>\n",
       "      <td>Loud 'N Clear&amp;trade; Personal Sound Amplifier</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>idea village</td>\n",
       "      <td>[]</td>\n",
       "      <td>2,938,573 in Beauty &amp; Personal Care (</td>\n",
       "      <td>[]</td>\n",
       "      <td>{'ASIN: ': '6546546450'}</td>\n",
       "      <td>All Beauty</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6546546450</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[No7 Lift &amp; Luminate Triple Action Serum 50ml ...</td>\n",
       "      <td></td>\n",
       "      <td>No7 Lift &amp;amp; Luminate Triple Action Serum 50...</td>\n",
       "      <td>[B01E7LCSL6, B008X5RVME]</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>872,854 in Beauty &amp; Personal Care (</td>\n",
       "      <td>[]</td>\n",
       "      <td>{'Shipping Weight:': '0.3 ounces (', 'ASIN: ':...</td>\n",
       "      <td>All Beauty</td>\n",
       "      <td>class=\"a-bordered a-horizontal-stripes  a-spa...</td>\n",
       "      <td></td>\n",
       "      <td>$44.99</td>\n",
       "      <td>7178680776</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[No7 Stay Perfect Foundation now stays perfect...</td>\n",
       "      <td></td>\n",
       "      <td>No7 Stay Perfect Foundation Cool Vanilla by No7</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>No7</td>\n",
       "      <td>[]</td>\n",
       "      <td>956,696 in Beauty &amp; Personal Care (</td>\n",
       "      <td>[B01B8BR0O8, B01B8BR0NO, B014MHXXM8]</td>\n",
       "      <td>{'Shipping Weight:': '3.5 ounces (', 'ASIN: ':...</td>\n",
       "      <td>All Beauty</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>$28.76</td>\n",
       "      <td>7250468162</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>Wella Koleston Perfect Hair Colour 44/44 Mediu...</td>\n",
       "      <td>[B0041PBXX8]</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>1,870,258 in Beauty &amp; Personal Care (</td>\n",
       "      <td>[]</td>\n",
       "      <td>{'\n",
       "    Item Weight: \n",
       "    ': '1.76 ounces', 'Sh...</td>\n",
       "      <td>All Beauty</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>7367905066</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[Lacto Calamine Skin Balance Daily Nourishing ...</td>\n",
       "      <td></td>\n",
       "      <td>Lacto Calamine Skin Balance Oil control 120 ml...</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>Pirmal Healthcare</td>\n",
       "      <td>[]</td>\n",
       "      <td>67,701 in Beauty &amp; Personal Care (</td>\n",
       "      <td>[3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...</td>\n",
       "      <td>{'Shipping Weight:': '12 ounces (', 'ASIN: ': ...</td>\n",
       "      <td>All Beauty</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>$12.15</td>\n",
       "      <td>7414204790</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category tech1                                        description fit  \\\n",
       "0       []        [Loud 'N Clear Personal Sound Amplifier allows...       \n",
       "1       []        [No7 Lift & Luminate Triple Action Serum 50ml ...       \n",
       "2       []        [No7 Stay Perfect Foundation now stays perfect...       \n",
       "3       []                                                       []       \n",
       "4       []        [Lacto Calamine Skin Balance Daily Nourishing ...       \n",
       "\n",
       "                                               title  \\\n",
       "0      Loud 'N Clear&trade; Personal Sound Amplifier   \n",
       "1  No7 Lift &amp; Luminate Triple Action Serum 50...   \n",
       "2    No7 Stay Perfect Foundation Cool Vanilla by No7   \n",
       "3  Wella Koleston Perfect Hair Colour 44/44 Mediu...   \n",
       "4  Lacto Calamine Skin Balance Oil control 120 ml...   \n",
       "\n",
       "                   also_buy tech2              brand feature  \\\n",
       "0                        []             idea village      []   \n",
       "1  [B01E7LCSL6, B008X5RVME]                               []   \n",
       "2                        []                      No7      []   \n",
       "3              [B0041PBXX8]                               []   \n",
       "4                        []        Pirmal Healthcare      []   \n",
       "\n",
       "                                    rank  \\\n",
       "0  2,938,573 in Beauty & Personal Care (   \n",
       "1    872,854 in Beauty & Personal Care (   \n",
       "2    956,696 in Beauty & Personal Care (   \n",
       "3  1,870,258 in Beauty & Personal Care (   \n",
       "4     67,701 in Beauty & Personal Care (   \n",
       "\n",
       "                                           also_view  \\\n",
       "0                                                 []   \n",
       "1                                                 []   \n",
       "2               [B01B8BR0O8, B01B8BR0NO, B014MHXXM8]   \n",
       "3                                                 []   \n",
       "4  [3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...   \n",
       "\n",
       "                                             details    main_cat  \\\n",
       "0                           {'ASIN: ': '6546546450'}  All Beauty   \n",
       "1  {'Shipping Weight:': '0.3 ounces (', 'ASIN: ':...  All Beauty   \n",
       "2  {'Shipping Weight:': '3.5 ounces (', 'ASIN: ':...  All Beauty   \n",
       "3  {'\n",
       "    Item Weight: \n",
       "    ': '1.76 ounces', 'Sh...  All Beauty   \n",
       "4  {'Shipping Weight:': '12 ounces (', 'ASIN: ': ...  All Beauty   \n",
       "\n",
       "                                        similar_item date   price        asin  \\\n",
       "0                                                                  6546546450   \n",
       "1   class=\"a-bordered a-horizontal-stripes  a-spa...       $44.99  7178680776   \n",
       "2                                                          $28.76  7250468162   \n",
       "3                                                                  7367905066   \n",
       "4                                                          $12.15  7414204790   \n",
       "\n",
       "                                            imageURL  \\\n",
       "0                                                 []   \n",
       "1                                                 []   \n",
       "2                                                 []   \n",
       "3  [https://images-na.ssl-images-amazon.com/image...   \n",
       "4  [https://images-na.ssl-images-amazon.com/image...   \n",
       "\n",
       "                                     imageURLHighRes  \n",
       "0                                                 []  \n",
       "1                                                 []  \n",
       "2                                                 []  \n",
       "3  [https://images-na.ssl-images-amazon.com/image...  \n",
       "4  [https://images-na.ssl-images-amazon.com/image...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b402792c",
   "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>asin</th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>overall</th>\n",
       "      <th>unixReviewTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0143026860</td>\n",
       "      <td>A1V6B6TNIC10QE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1424304000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0143026860</td>\n",
       "      <td>A2F5GHSXFQ0W6J</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1418860800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0143026860</td>\n",
       "      <td>A1572GUYS7DGSR</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1407628800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0143026860</td>\n",
       "      <td>A1PSGLFK1NSVO</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1362960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0143026860</td>\n",
       "      <td>A6IKXKZMTKGSC</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1324771200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         asin      reviewerID  overall  unixReviewTime\n",
       "0  0143026860  A1V6B6TNIC10QE      1.0      1424304000\n",
       "1  0143026860  A2F5GHSXFQ0W6J      4.0      1418860800\n",
       "2  0143026860  A1572GUYS7DGSR      4.0      1407628800\n",
       "3  0143026860   A1PSGLFK1NSVO      5.0      1362960000\n",
       "4  0143026860   A6IKXKZMTKGSC      5.0      1324771200"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "695362e3",
   "metadata": {},
   "source": [
    "## 資料整理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b552fb23",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings['DATE'] = pd.to_datetime(ratings['unixReviewTime'], unit='s')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "054eeba4",
   "metadata": {},
   "source": [
    "## 資料切分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "284b14a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_trainings = ratings[\n",
    "    (ratings['DATE'] < '2018-09-01')\n",
    "]\n",
    "ratings_testings = ratings[\n",
    "    (ratings['DATE'] >= '2018-09-01') & \n",
    "    (ratings['DATE'] <= '2018-09-30')\n",
    "]\n",
    "ratings_testings_by_user = ratings_testings.groupby('reviewerID').agg(list).reset_index()[['reviewerID', 'asin']].to_dict('records')\n",
    "ratings_testings_by_user = { rating['reviewerID']: rating['asin'] for rating in ratings_testings_by_user }\n",
    "users = list(ratings_testings_by_user.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6c76061",
   "metadata": {},
   "source": [
    "## 產生推薦"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9280a043",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data converted\n",
      "data inverted\n",
      "sim data prepared\n",
      "get 10 recommendation items for for user: ['A100XQFWKQ30O2', 'A103T1QOGFCSEH', 'A106UKKSJ2KXPF', 'A10A7GV4D5A11V', 'A1119JJ37ZLB8R', 'A113UOOLBSZN52', 'A12M4U7WK4ALCR', 'A12T8YTW6VWT7S', 'A1364JXGKB46MM', 'A137DALOQFKBTI', 'A13FEZ3WV7S2EY', 'A13IV4I1B0RXMG', 'A13JU88JAHN72I', 'A13K55R6VH1OOD', 'A13P7VFU075A', 'A13SWYE4QLB6NG', 'A13ZTQ0Q4ATA41', 'A142EDN04OD62U', 'A142I22FIC8MZK', 'A14834QTII5TLT', 'A14A447VPACTBC', 'A14AP6MN5XO6LB', 'A14CLF25IX25US', 'A14LYXC3HTBAHI', 'A14VUW4KZ34EOE', 'A14Y32P26G9YL', 'A157T25PBS7MX4', 'A15HZDSERD85C8', 'A15JJ8J1FGADIX', 'A15ZCL70JXXH89', 'A1617KN2IAWZ6J', 'A16E0O88262HKA', 'A16NSZ58PTVIYF', 'A16UGDXRTDLJG5', 'A16X9HR3UFQQXY', 'A16Y7V1CZCWKFV', 'A174YOBOSW9WDN', 'A1786SKRAJXH86', 'A17K2BUZ20WD2', 'A17LYRFV645L0V', 'A18LNGVXDZBTUR', 'A19503XX7GU6J2', 'A19HVHRZDYFEOP', 'A19JM38B861BO4', 'A19KGY3W1P0MHT', 'A19KJPOF3QOJ24', 'A19U5RT4Q8Q1T6', 'A1AQNQUNX8B6JT', 'A1ARU0IN6RUFFD', 'A1BB4635KSKDMV', 'A1BCC7X6KRTBG0', 'A1BPJ56BXENC7', 'A1C0RI6W65FC8B', 'A1C60LJGZVEK27', 'A1CB2SMS3TD44Z', 'A1CNSU8P68CPLT', 'A1CSDWBYUJBCJZ', 'A1CTNNVIYHO25P', 'A1CTQF97HFYOZB', 'A1D5C2H9UFZHPU', 'A1D5UVH72Z6NJ1', 'A1DAUVZRVSLLNW', 'A1DCZ9882PXLVB', 'A1DZ63D35BNT86', 'A1E0QCKB4R2X8X', 'A1E76OZZ5YUWVA', 'A1EBHZPAYMILW9', 'A1EJVNGBY5GM5I', 'A1ER0356V2DSKW', 'A1EY751AUVO5NZ', 'A1EZFX1TRHSAJX', 'A1F08GM4B97FV', 'A1F6EFMBK795DJ', 'A1FC8Y896NZP4T', 'A1FKIAKVOBNN7Y', 'A1FKUBRPLG6Y8I', 'A1G2HL4W8B3TA0', 'A1G8Z9IKJ0BD1M', 'A1GEJ55RJGD7JW', 'A1GIYGD23YIDTN', 'A1GLG2IZBZHRAJ', 'A1H1T82FPO20DN', 'A1H7JDZ9BKTKGC', 'A1HBROZPSV5KT6', 'A1HBXMUIHTAIU2', 'A1I32A4SA9W2L2', 'A1IDJ6KR8CQV9I', 'A1II0TTUUE2C7Y', 'A1ILEZFIVOU0EP', 'A1ITJ5M7I9G7FR', 'A1IWTXM1SJWHDN', 'A1J2NTTE63GNG2', 'A1JRREJ5D62LFF', 'A1KGMTEXL70WF6', 'A1KWUQZJ1Z6MN2', 'A1LXJ8XCGNBXOF', 'A1M8YMS915ZK0B', 'A1NDRR3G9SJF18', 'A1NE6K8PXW780O', 'A1NEG0FRW5FDBL', 'A1NN82RWB6PXJW', 'A1NY67EVWD6CD6', 'A1OB41VQHXZ0R8', 'A1OJ2IXX8F7Q7X', 'A1OTIUDE7LWAGT', 'A1P1OHYJOOZX5P', 'A1PFDA9SL15S4O', 'A1PKBKALGLSX0Z', 'A1PMLXHE863GR6', 'A1PR178735XC70', 'A1PXO67EUM5HJQ', 'A1QHS58JVFLF3I', 'A1QPKRHCAOQVZ5', 'A1R2TTULP0Z2BM', 'A1R8GBYBJWWWU0', 'A1RAXJN2Q4V20X', 'A1RCMCAJR2LJA4', 'A1RE2XA5HQD1D3', 'A1RMLPJ1PF8LGV', 'A1RX2ODQRQZVSI', 'A1S3NMNTSO6GW', 'A1S6OQV823B53J', 'A1SA3N793WT2LM', 'A1SA932MLERVQY', 'A1SCZBXGPJ2615', 'A1SMXRCNRO4ZQM', 'A1T1N2585E1T0D', 'A1TEKLMF4V1X2C', 'A1TF44D9KYAD60', 'A1TGP1VTM3KCTI', 'A1TK5M6EM3GS3C', 'A1TO3GV14TGF2R', 'A1TP9JHTIY468V', 'A1UAOE8KO7Q1DZ', 'A1UDCDY6ZNE83T', 'A1UUNKRU1DTZTL', 'A1V0UJQ4XGAJ3H', 'A1VBZLO2JGYU1B', 'A1VC6YCAFKV6Q', 'A1VVEX78B2TYBX', 'A1VVQ2DFL0QLXF', 'A1VYWSQ2OXLH98', 'A1WEFBEJ7OHSVZ', 'A1WOX40GUAFDKW', 'A1WSZED2O5MA5T', 'A1XBJBT6HORBLA', 'A1XHN6JF8A8W69', 'A1XWCTARAQ0679', 'A1XZJ85PKMAZP0', 'A1YALF6PWOMS6S', 'A1YEO5KEW7M2GF', 'A1YQ33635DJEG5', 'A1YSAFP6T3JZ1C', 'A1YTPCNXQ2LJ5Z', 'A1YVXZK51DEDPC', 'A1Z3GE09RDY6O7', 'A1Z6IPI0G3QIUR', 'A1ZVXL7K0XY3S6', 'A1ZXYITUZM3Q6A', 'A202BVEB76NTMK', 'A205S0BTPEFI0I', 'A20MSUD02BETH2', 'A20P2XESU3G3EC', 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    {
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      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from itertools import combinations\n",
    "from collections import defaultdict\n",
    "\n",
    "def recommender(training_data, users=[], k=10):\n",
    "\n",
    "    # loading data from dataframe\n",
    "    # item_to_users dict:\n",
    "    # {\n",
    "    #   'item': {\n",
    "    #       'user': ratings...\n",
    "    #   }...\n",
    "    # }\n",
    "    item_to_users = defaultdict(dict)\n",
    "    for _, row in training_data.iterrows():\n",
    "        row = dict(row)\n",
    "        user = row['reviewerID']\n",
    "        item = row['asin']\n",
    "        rating = float(row['overall'])\n",
    "        item_to_users[item][user] = rating\n",
    "\n",
    "    print(\"data converted\")\n",
    "\n",
    "    user_to_items = defaultdict(dict)\n",
    "    for item, rating_users in item_to_users.items():\n",
    "        for user, rating in rating_users.items():\n",
    "            user_to_items[user][item] = rating\n",
    "\n",
    "    print(\"data inverted\")\n",
    "\n",
    "    init_sim = lambda: [0, 0, 0]\n",
    "    factory = lambda: defaultdict(init_sim)\n",
    "    pre_item_similarity = defaultdict(factory)\n",
    "    for user, items in user_to_items.items():\n",
    "        if len(items) > 1:\n",
    "            for i1, i2 in combinations(items.keys(), 2):\n",
    "                xy = items[i1] * items[i2]\n",
    "                xx = items[i1] ** 2\n",
    "                yy = items[i2] ** 2\n",
    "                pre_item_similarity[i1][i2][0] += xy\n",
    "                pre_item_similarity[i1][i2][1] += xx\n",
    "                pre_item_similarity[i1][i2][2] += yy\n",
    "\n",
    "                pre_item_similarity[i2][i1][0] += xy\n",
    "                pre_item_similarity[i2][i1][1] += xx\n",
    "                pre_item_similarity[i2][i1][2] += yy\n",
    "\n",
    "    print(\"sim data prepared\")\n",
    "\n",
    "    item_similarity = {}\n",
    "    for src_item in pre_item_similarity:\n",
    "        item_similarity_order = []\n",
    "        for dst_item, val in pre_item_similarity[src_item].items():\n",
    "            xy = val[0]\n",
    "            xx = val[1]\n",
    "            yy = val[2]\n",
    "            div = ((xx*yy) ** 0.5)\n",
    "            if div == 0:\n",
    "                continue\n",
    "            similarity = xy / div\n",
    "            if similarity < 0:\n",
    "                continue\n",
    "            for i, s in enumerate(item_similarity_order):\n",
    "                target_similarity = s[1]\n",
    "                if target_similarity < similarity:\n",
    "                    item_similarity_order.insert(i, (dst_item, similarity))\n",
    "                    break\n",
    "            else:\n",
    "                item_similarity_order.append((dst_item, similarity))\n",
    "        item_similarity[src_item] = item_similarity_order\n",
    "\n",
    "    print(f\"get {k} recommendation items for for user: {users}\")\n",
    "\n",
    "    recommendation = {}\n",
    "    for user in users:\n",
    "        items = []\n",
    "        items_set = set()\n",
    "        stop = False\n",
    "        user_has_rated = set(user_to_items[user])\n",
    "        for item in user_has_rated:\n",
    "            if item in item_similarity:\n",
    "                for sim_item, _ in item_similarity[item]:\n",
    "                    # skip the item user has rated\n",
    "                    if sim_item not in user_has_rated and sim_item not in items_set:\n",
    "                        items.append(sim_item)\n",
    "                        items_set.add(sim_item)\n",
    "                    if len(items) >= k:\n",
    "                        stop = True\n",
    "                        break\n",
    "                if stop:\n",
    "                    break\n",
    "        recommendation[user] = items\n",
    "    return recommendation    \n",
    "\n",
    "ratings_by_user = recommender(ratings_trainings, users)\n",
    "ratings_by_user"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04a4ca9e",
   "metadata": {},
   "source": [
    "## 結果評估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ee763747",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def evaluate(ratings_testings_by_user={}, ratings_by_user={}, method=None):\n",
    "    '''\n",
    "    * ratings_testings_by_user: dict 真實被購買的商品資料（2018-09-01 以後資料）\n",
    "    * ratings_by_user: dict 利用訓練資料學習的推薦商品\n",
    "    * method: str\n",
    "    * score: float\n",
    "    '''\n",
    "    total = 0\n",
    "    for d in ratings_testings_by_user:\n",
    "        if d in ratings_by_user:\n",
    "            total += len(set(ratings_by_user[d]) & set(ratings_testings_by_user[d]))\n",
    "\n",
    "    score = total / len(ratings_testings)\n",
    "    return score\n",
    "\n",
    "evaluate(ratings_testings_by_user, ratings_by_user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53055ac2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d2a358f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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