item-based-collaborative-recommender-system-on-goodreads.ipynb

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   },
   "source": [
    "# Book recommender system"
   ]
  },
  {
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   "execution_count": 5,
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    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "bookDF=pd.read_csv('books.csv')\n",
    "bookDF=bookDF.drop(['image_url','small_image_url','title','best_book_id','isbn','isbn13'],axis=1)\n",
    "ratingsDF = pd.read_csv('ratings.csv')\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
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       "   id  book_id  work_id  books_count                      authors  \\\n",
       "0   1  2767052  2792775          272              Suzanne Collins   \n",
       "1   2        3  4640799          491  J.K. Rowling, Mary GrandPré   \n",
       "2   3    41865  3212258          226              Stephenie Meyer   \n",
       "3   4     2657  3275794          487                   Harper Lee   \n",
       "4   5     4671   245494         1356          F. Scott Fitzgerald   \n",
       "\n",
       "   original_publication_year                            original_title  \\\n",
       "0                     2008.0                          The Hunger Games   \n",
       "1                     1997.0  Harry Potter and the Philosopher's Stone   \n",
       "2                     2005.0                                  Twilight   \n",
       "3                     1960.0                     To Kill a Mockingbird   \n",
       "4                     1925.0                          The Great Gatsby   \n",
       "\n",
       "  language_code  average_rating  ratings_count  work_ratings_count  \\\n",
       "0           eng            4.34        4780653             4942365   \n",
       "1           eng            4.44        4602479             4800065   \n",
       "2         en-US            3.57        3866839             3916824   \n",
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       "\n",
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       "0                   155254      66715     127936     560092    1481305   \n",
       "1                    75867      75504     101676     455024    1156318   \n",
       "2                    95009     456191     436802     793319     875073   \n",
       "3                    72586      60427     117415     446835    1001952   \n",
       "4                    51992      86236     197621     606158     936012   \n",
       "\n",
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       "0    2706317  \n",
       "1    3011543  \n",
       "2    1355439  \n",
       "3    1714267  \n",
       "4     947718  "
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    "bookDF['original_title']=bookDF['original_title'].str.lower()"
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       "   id  book_id  work_id  books_count                      authors  \\\n",
       "0   1  2767052  2792775          272              Suzanne Collins   \n",
       "1   2        3  4640799          491  J.K. Rowling, Mary GrandPré   \n",
       "2   3    41865  3212258          226              Stephenie Meyer   \n",
       "3   4     2657  3275794          487                   Harper Lee   \n",
       "4   5     4671   245494         1356          F. Scott Fitzgerald   \n",
       "\n",
       "   original_publication_year                            original_title  \\\n",
       "0                     2008.0                          the hunger games   \n",
       "1                     1997.0  harry potter and the philosopher's stone   \n",
       "2                     2005.0                                  twilight   \n",
       "3                     1960.0                     to kill a mockingbird   \n",
       "4                     1925.0                          the great gatsby   \n",
       "\n",
       "  language_code  average_rating  ratings_count  work_ratings_count  \\\n",
       "0           eng            4.34        4780653             4942365   \n",
       "1           eng            4.44        4602479             4800065   \n",
       "2         en-US            3.57        3866839             3916824   \n",
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       "\n",
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       "0                   155254      66715     127936     560092    1481305   \n",
       "1                    75867      75504     101676     455024    1156318   \n",
       "2                    95009     456191     436802     793319     875073   \n",
       "3                    72586      60427     117415     446835    1001952   \n",
       "4                    51992      86236     197621     606158     936012   \n",
       "\n",
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       "0    2706317  \n",
       "1    3011543  \n",
       "2    1355439  \n",
       "3    1714267  \n",
       "4     947718  "
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       "   book_id  user_id  rating\n",
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       "      <td>314</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>173</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>439</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>588</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>439</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3662</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  rating\n",
       "book_id                 \n",
       "1            314       5\n",
       "2           1169       3\n",
       "3            314       3\n",
       "4            439       5\n",
       "5            314       4\n",
       "6            173       5\n",
       "7            439       3\n",
       "8            588       3\n",
       "9            439       3\n",
       "10          3662       4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdf = ratingsDF\n",
    "testdf=testdf[['user_id','rating']].groupby(testdf['book_id'])\n",
    "testdf.first().head(10)\n",
    "#print(testdf.get_group(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "24d2da87-a5be-4256-b12d-592be11dcdfc",
    "_uuid": "80ed273190135375b7be2b5956e47d3f02882229"
   },
   "source": [
    "This part of the code is responsible for building the item-item similarity matrix\n",
    "We first convert build a list of dictionary.\n",
    "By creating groups of books coreeEach dictionary corresponds to a single book. The user_id is the key, while rating given by the user for the book is its value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "cc5e5429-cf93-42fe-90d2-c784f20cbe6b",
    "_uuid": "4cb2efbf0c8a40c76c44356be576501b362726d1"
   },
   "outputs": [],
   "source": [
    "listOfDictonaries=[]\n",
    "#for item-item based similarity matrix this will be used\n",
    "indexMap = {}\n",
    "reverseIndexMap = {}\n",
    "#to keep tract of the index\n",
    "ptr=0;\n",
    "testdf = ratingsDF\n",
    "#we make a testdf to not disturb the ratings matrix\n",
    "testdf=testdf[['user_id','rating']].groupby(testdf['book_id'])\n",
    "#this will group all the books together so that there will be users and their ratings for each group(book)\n",
    "#we are using the groupby method to group users and ratings according to books they have rated\n",
    "for groupKey in testdf.groups.keys():\n",
    "    #testdf.group.keys() will give all the groups formed by the groupby method and groupKey will take every group one by one\n",
    "    tempDict={}\n",
    "    #temp dict for temporary storing data in a dictionary to add to listofDictionaries\n",
    "    groupDF = testdf.get_group(groupKey)\n",
    "    #get_group method will fetch all the data corresponding to the groupKey and store it in groupDf\n",
    "    for i in range(0,len(groupDF)):\n",
    "        #loop through all the entries of the group to users of a book\n",
    "        tempDict[groupDF.iloc[i,0]]=groupDF.iloc[i,1]\n",
    "        #making a dict by assigning the user the rating it has given\n",
    "        #each tempdict corresponds to a single book\n",
    "    indexMap[ptr]=groupKey\n",
    "    #keeping track of the index\n",
    "    reverseIndexMap[groupKey] = ptr\n",
    "    ptr=ptr+1\n",
    "    listOfDictonaries.append(tempDict)\n",
    "    #adding the temp dict of a book to listOfDictionaries with all the other dicts of books"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{314: 5, 439: 3, 588: 5, 1169: 4, 1185: 4, 2077: 4, 2487: 4, 2900: 5, 3662: 4, 3922: 5, 5379: 5, 5461: 3, 5885: 5, 6630: 5, 7563: 3, 9246: 1, 10140: 4, 10146: 5, 10246: 4, 10335: 4, 10610: 5, 10944: 5, 11854: 4, 11927: 4, 12471: 5, 13282: 5, 13544: 5, 15494: 5, 16377: 4, 16913: 5, 17434: 5, 17663: 5, 17984: 5, 18031: 5, 18313: 5, 18361: 4, 20076: 3, 20467: 3, 20848: 5, 21228: 5, 21487: 4, 21713: 5, 22602: 4, 23576: 4, 23612: 4, 24326: 5, 24389: 4, 24499: 5, 24834: 4, 24845: 5, 25164: 4, 25182: 5, 25214: 4, 26145: 4, 26629: 4, 26661: 4, 28158: 4, 28767: 5, 29123: 3, 29703: 5, 30681: 5, 31001: 4, 32055: 4, 32305: 5, 32592: 4, 32635: 5, 32748: 5, 32923: 5, 33065: 4, 33697: 4, 33716: 5, 33872: 5, 33890: 3, 37284: 5, 37834: 5, 38080: 4, 38082: 5, 38475: 4, 39423: 3, 41074: 3, 42404: 5, 43985: 4, 44243: 2, 44397: 5, 45269: 4, 45493: 5, 46977: 4, 47476: 4, 47746: 5, 47800: 5, 48482: 3, 49298: 4, 50104: 5, 50342: 3, 51166: 4, 51460: 3, 51480: 1, 51838: 5, 52036: 2, 53245: 4}\n"
     ]
    }
   ],
   "source": [
    "print(listOfDictonaries[0])\n",
    "#the ratings of the first book users.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#print(indexMap)\n",
    "#print(reverseIndexMap)\n",
    "#print(testdf.groups.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>user_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>314</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>439</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>588</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1169</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1185</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981660</th>\n",
       "      <td>366</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981661</th>\n",
       "      <td>806</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981662</th>\n",
       "      <td>1755</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981663</th>\n",
       "      <td>2824</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981664</th>\n",
       "      <td>5505</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  rating\n",
       "0           314       5\n",
       "1           439       3\n",
       "2           588       5\n",
       "3          1169       4\n",
       "4          1185       4\n",
       "...         ...     ...\n",
       "981660      366       4\n",
       "981661      806       5\n",
       "981662     1755       5\n",
       "981663     2824       4\n",
       "981664     5505       3\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cbf14425-def6-45e8-8ba7-b314dacab495",
    "_uuid": "98221c85b131d8ad3661980088819fdfc6e2a487"
   },
   "source": [
    "We then use sklearn's DictVectorizer() function to create vectors corresponding to each book. We are trying to create a vector space with users as column vectors. Each point in the vector space represents a book. Rating of the book given an user is its magnitude. We then calculate similarity/distance between books in this vector space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "2a843a93-e1f4-4cae-afc6-fcda82b22a62",
    "_uuid": "2d9ac927b41da124e8ec17c00d294152f01a1767"
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer\n",
    "dictVectorizer = DictVectorizer(sparse=True)\n",
    "#sparse is True means the matrix will not have NaN values instead it'll have zeros in place.\n",
    "vector = dictVectorizer.fit_transform(listOfDictonaries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8e3f3614-04db-421d-ab1b-1b93896df754",
    "_uuid": "67c43bfbe795091ee569046d3de610f7e782d47f"
   },
   "source": [
    "We finally use sklearn's consine_similarity function to calculate pairwise similarity matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "7dfdb814-1c70-4980-9359-1de44344beb9",
    "_uuid": "e6b8e3717bb298b57e4e4fefb800873451f3ed25"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "pairwiseSimilarity = cosine_similarity(vector)\n",
    "#define: A.B=||A||.||B||.cos@\n",
    "#where cos@ is the simiarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2c17a12a-7365-4941-a902-c91eca0f3b99",
    "_uuid": "b7264f30af7c17d67fae5cb471389bd0f0804a49"
   },
   "source": [
    "At last we define a function that takes an input id and outputs 5 top most recommendations. Please note that this id is the column id in book.csv. Values of id are in  [1,10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "8194dbfc-af78-420d-a1c4-a4aea1f7ea2f",
    "_uuid": "32b81248430f914ed128e016715fb4eff584ac7b"
   },
   "outputs": [],
   "source": [
    "def printBookDetails(bookID):\n",
    "    print(\"Title:\", bookDF[bookDF['id']==bookID]['original_title'].values[0])\n",
    "    print(\"Author:\",bookDF[bookDF['id']==bookID]['authors'].values[0])\n",
    "    print(\"Printing Book-ID:\",bookID)\n",
    "    print(\"=================++++++++++++++=========================\")\n",
    "\n",
    "\n",
    "def getTopRecommandations(bookID):\n",
    "    row = reverseIndexMap[bookID]\n",
    "    print(\"------INPUT BOOK--------\")\n",
    "    printBookDetails(bookID)\n",
    "    print(\"-------RECOMMENDATIONS----------\")\n",
    "    similarBookIDs = [printBookDetails(indexMap[i]) for i in np.argsort(pairwiseSimilarity[row])[-7:-2][::-1]]\n",
    "    print(similarBookIDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getTopRecommandationsname(bookname,count):\n",
    "    bookID=(bookDF[bookDF['original_title']==bookname]['id'].values[0])\n",
    "    row = reverseIndexMap[bookID]\n",
    "    #reverse indexing of the simialarity row of that book in the pairwise similarity matrix \n",
    "    print(\"------INPUT BOOK--------\")\n",
    "    printBookDetails(bookID)\n",
    "    print(\"-------RECOMMENDATIONS----------\")\n",
    "    #print((np.argsort(pairwiseSimilarity[row])[-7:-2][::-1]))\n",
    "    #this is a np.array of length 10000 \n",
    "    count+=2\n",
    "    for i in np.argsort(pairwiseSimilarity[row])[-count:-2][::-1]:\n",
    "        print(i)\n",
    "        printBookDetails(indexMap[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------INPUT BOOK--------\n",
      "Title: fifty shades of grey\n",
      "Author: E.L. James\n",
      "Printing Book-ID: 34\n",
      "=================++++++++++++++=========================\n",
      "-------RECOMMENDATIONS----------\n",
      "98\n",
      "Title: fifty shades darker\n",
      "Author: E.L. James\n",
      "Printing Book-ID: 99\n",
      "=================++++++++++++++=========================\n",
      "95\n",
      "Title: fifty shades freed\n",
      "Author: E.L. James\n",
      "Printing Book-ID: 96\n",
      "=================++++++++++++++=========================\n",
      "144\n",
      "Title: deception point\n",
      "Author: Dan Brown\n",
      "Printing Book-ID: 145\n",
      "=================++++++++++++++=========================\n",
      "2\n",
      "Title: twilight\n",
      "Author: Stephenie Meyer\n",
      "Printing Book-ID: 3\n",
      "=================++++++++++++++=========================\n"
     ]
    }
   ],
   "source": [
    "getTopRecommandationsname('fifty shades of grey',4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------INPUT BOOK--------\n",
      "Title: the brethren\n",
      "Author: John Grisham\n",
      "Printing Book-ID: 1245\n",
      "=================++++++++++++++=========================\n",
      "-------RECOMMENDATIONS----------\n",
      "1036\n",
      "Title: the street lawyer\n",
      "Author: John Grisham\n",
      "Printing Book-ID: 1037\n",
      "=================++++++++++++++=========================\n",
      "1387\n",
      "Title: the last juror\n",
      "Author: John Grisham\n",
      "Printing Book-ID: 1388\n",
      "=================++++++++++++++=========================\n",
      "1056\n",
      "Title: the testament\n",
      "Author: John Grisham\n",
      "Printing Book-ID: 1057\n",
      "=================++++++++++++++=========================\n",
      "1300\n",
      "Title: the broker\n",
      "Author: John Grisham\n",
      "Printing Book-ID: 1301\n",
      "=================++++++++++++++=========================\n"
     ]
    }
   ],
   "source": [
    "getTopRecommandationsname('the brethren',4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "54b79700-8cc2-47c3-86de-ea7bddef9ca4",
    "_uuid": "5e4142538908f441bbf0c73d9d8241c64223c21b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------INPUT BOOK--------\n",
      "Title: the brethren\n",
      "Author: John Grisham\n",
      "Printing Book-ID: 1245\n",
      "=================++++++++++++++=========================\n",
      "-------RECOMMENDATIONS----------\n",
      "Title: the street lawyer\n",
      "Author: John Grisham\n",
      "Printing Book-ID: 1037\n",
      "=================++++++++++++++=========================\n",
      "Title: the last juror\n",
      "Author: John Grisham\n",
      "Printing Book-ID: 1388\n",
      "=================++++++++++++++=========================\n",
      "Title: the testament\n",
      "Author: John Grisham\n",
      "Printing Book-ID: 1057\n",
      "=================++++++++++++++=========================\n",
      "Title: the broker\n",
      "Author: John Grisham\n",
      "Printing Book-ID: 1301\n",
      "=================++++++++++++++=========================\n",
      "Title: the king of torts\n",
      "Author: John Grisham\n",
      "Printing Book-ID: 1407\n",
      "=================++++++++++++++=========================\n",
      "[None, None, None, None, None]\n"
     ]
    }
   ],
   "source": [
    "getTopRecommandations(1245)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "e9f41068-c1b8-4e40-adc8-56469d8b6096",
    "_uuid": "ef535e03a5ae0446c6fe2116619dad575e92c3b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------INPUT BOOK--------\n",
      "Title: losing my virginity: how i've survived, had fun, and made a fortune doing business my way\n",
      "Author: Richard Branson\n",
      "Printing Book-ID: 4536\n",
      "=================++++++++++++++=========================\n",
      "-------RECOMMENDATIONS----------\n",
      "Title: jack: straight from the gut\n",
      "Author: Jack Welch, John A. Byrne, Mike Barnicle\n",
      "Printing Book-ID: 8454\n",
      "=================++++++++++++++=========================\n",
      "Title: the google story: inside the hottest business, media, and technology success of our time\n",
      "Author: David A. Vise\n",
      "Printing Book-ID: 7146\n",
      "=================++++++++++++++=========================\n",
      "Title: in the plex: how google thinks, works, and shapes our lives\n",
      "Author: Steven Levy\n",
      "Printing Book-ID: 6363\n",
      "=================++++++++++++++=========================\n",
      "Title: shoe dog: a memoir by the creator of nike\n",
      "Author: Phil Knight\n",
      "Printing Book-ID: 6583\n",
      "=================++++++++++++++=========================\n",
      "Title: one up on wall street : how to use what you already know to make money in the market\n",
      "Author: Peter Lynch, John Rothchild\n",
      "Printing Book-ID: 8903\n",
      "=================++++++++++++++=========================\n",
      "[None, None, None, None, None]\n"
     ]
    }
   ],
   "source": [
    "getTopRecommandations(4536)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "cbf4eed1-c5d5-48ff-ae16-20398bc9d4e6",
    "_uuid": "494d9fc09b3f8588087e189b0c6148f5295cb663",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------INPUT BOOK--------\n",
      "Title: fifty shades darker\n",
      "Author: E.L. James\n",
      "Printing Book-ID: 99\n",
      "=================++++++++++++++=========================\n",
      "-------RECOMMENDATIONS----------\n",
      "Title: fifty shades of grey\n",
      "Author: E.L. James\n",
      "Printing Book-ID: 34\n",
      "=================++++++++++++++=========================\n",
      "Title: me before you\n",
      "Author: Jojo Moyes\n",
      "Printing Book-ID: 112\n",
      "=================++++++++++++++=========================\n",
      "Title: big little lies\n",
      "Author: Liane Moriarty\n",
      "Printing Book-ID: 262\n",
      "=================++++++++++++++=========================\n",
      "Title: the husband's secret \n",
      "Author: Liane Moriarty\n",
      "Printing Book-ID: 235\n",
      "=================++++++++++++++=========================\n",
      "Title: the nightingale\n",
      "Author: Kristin Hannah\n",
      "Printing Book-ID: 267\n",
      "=================++++++++++++++=========================\n",
      "[None, None, None, None, None]\n"
     ]
    }
   ],
   "source": [
    "getTopRecommandations(99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------INPUT BOOK--------\n",
      "Title: think and grow rich\n",
      "Author: Napoleon Hill\n",
      "Printing Book-ID: 680\n",
      "=================++++++++++++++=========================\n",
      "-------RECOMMENDATIONS----------\n",
      "1683\n",
      "Title: nan\n",
      "Author: George S. Clason\n",
      "Printing Book-ID: 1684\n",
      "=================++++++++++++++=========================\n",
      "2504\n",
      "Title: the greatest salesman in the world\n",
      "Author: Og Mandino\n",
      "Printing Book-ID: 2505\n",
      "=================++++++++++++++=========================\n"
     ]
    }
   ],
   "source": [
    "getTopRecommandationsname('think and grow rich',2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
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