Challenge1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Challenge 1\n",
    "### Categorizing our content & developing strategy\n",
    "------------------\n",
    "\n",
    "**Problem Statement:** Analyze the given dataset and answer the following questions:\n",
    "\n",
    "1. What are the major shortcomings of using RAKE for keyword extraction?  \n",
    "2. How would you improve it to achieve better search results? </br>\n",
    "3. In addition to improving search, we are also interested in improving personalized\n",
    "recommendations that can be found in the “For You” section on the Discover screen.\n",
    "Which data points would you extract from books in blinks or in general from user and\n",
    "tracking data to be used in recommendation algorithms and why? Please list the data\n",
    "points in an order that is in line with our approach of maximizing value in an agile way. </br>\n",
    "4. By looking at the data we have around our content (assume we extracted the data points\n",
    "you listed in the previous question) and the mobile tracking data, which recommender\n",
    "system method would you employ and why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methodology\n",
    "------------------\n",
    "\n",
    "In order to analyse and understand the dataset, we'll categorize the analysis in mainly two parts:\n",
    "1. *Univariate analysis:* Helps in exploring features individually to detect anomalies (outliers, errors) etc.\n",
    "2. *Multivariate analysis:* Helps in exploring the inter-impact of features across multiple variables.\n",
    "\n",
    "Since, the questions revolve around analyzing category specific behaviour (search / discover), we'll try to categorize the variable distribution by these two variables in order to determine category specific behaviour.\n",
    "\n",
    "**Note:** Bivariate analysis is also widely used method in exploring behaviour of two variables.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary : Dataset\n",
    "-----------------------\n",
    "\n",
    "1. The given dataset(s): \n",
    "    * discover_events: represents a small sample of actual blinkist tracking data.\n",
    "    * book_json: represents meta information for the books (blinks) available at blinkist.\n",
    "2. The dataset given is for one week from `2018-11-07` to `2018-11-16`.\n",
    "3. The tracking data has 924256 entries and 12 features.\n",
    "4. 70% of the events in the given dataset belong to search. Search looks like an essential component for blinkist for content discovery.\n",
    "\n",
    "**Note:** The data dictionary doesn't not contain information for all the given features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Load libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# read the data\n",
    "discover_events = pd.read_csv('discover_events.csv', parse_dates=['dim_date_id','event_timestamp','arrival_timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dim_date_id          0.000000\n",
       "func_sha1            0.002534\n",
       "event_type           0.000000\n",
       "category             0.000000\n",
       "depth                0.028149\n",
       "action               0.000000\n",
       "content              0.163694\n",
       "screen_url           0.028149\n",
       "screenurl_params     0.028149\n",
       "event_timestamp      0.000000\n",
       "arrival_timestamp    0.000000\n",
       "session_id           0.000819\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# missing values\n",
    "discover_events.isna().sum(axis=0) / discover_events.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that `content` has 16% missing values. Let's look at the missing values distribution category-wise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>dim_date_id</th>\n",
       "      <th>func_sha1</th>\n",
       "      <th>event_type</th>\n",
       "      <th>depth</th>\n",
       "      <th>action</th>\n",
       "      <th>content</th>\n",
       "      <th>screen_url</th>\n",
       "      <th>screenurl_params</th>\n",
       "      <th>event_timestamp</th>\n",
       "      <th>arrival_timestamp</th>\n",
       "      <th>session_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>search</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2342.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26017.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151295.0</td>\n",
       "      <td>26017.0</td>\n",
       "      <td>26017.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>757.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flex-discover</th>\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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               dim_date_id  func_sha1  event_type    depth  action   content  \\\n",
       "category                                                                       \n",
       "search                 0.0     2342.0         0.0  26017.0     0.0  151295.0   \n",
       "flex-discover          0.0        0.0         0.0      0.0     0.0       0.0   \n",
       "\n",
       "               screen_url  screenurl_params  event_timestamp  \\\n",
       "category                                                       \n",
       "search            26017.0           26017.0              0.0   \n",
       "flex-discover         0.0               0.0              0.0   \n",
       "\n",
       "               arrival_timestamp  session_id  \n",
       "category                                      \n",
       "search                       0.0       757.0  \n",
       "flex-discover                0.0         0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discover_events.set_index('category').isna().sum(level=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting! We see that all the missing values are coming from search category. In further analysis, we discovered that `content` variable ends up being missing because of the following search events (search attempt events): <br/>\n",
    "\n",
    "|         event-type      |frequency | \n",
    "--------------------------|----------|\n",
    "SearchViewedSimplified    |     81715 \n",
    "SearchDismissedSimplified   |   43359\n",
    "search-2-trigger            | 11591\n",
    "search-3-book-open          |  5473\n",
    "search-1-open               |  3775\n",
    "search-4-clear              |  2227\n",
    "search-2-book-add           |  1660\n",
    "search-5-dismiss            |  1291\n",
    "SearchClearedSimplified     |   138\n",
    "SearchTriggeredSimplified   |    66\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Analysis\n",
    "\n",
    "In this section, we'll analyse some of the variable which seems influential in the given problem discovery."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a22455bd0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a234e0ed0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discover_events['category'].value_counts(normalize=True).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the data contains almost 70% `search` events. It seems like search is a quite an important channel for blinkist and the user for content discovery. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1163bd850>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'action', data=discover_events)\n",
    "_, _ = plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "open-book      0.396074\n",
       "trigger        0.302783\n",
       "view           0.088412\n",
       "add-book       0.072103\n",
       "clear          0.065127\n",
       "dismiss        0.046912\n",
       "2-trigger      0.012541\n",
       "3-book-open    0.005922\n",
       "1-open         0.004084\n",
       "4-clear        0.002410\n",
       "2-book-add     0.001796\n",
       "5-dismiss      0.001397\n",
       "book-add       0.000439\n",
       "Name: action, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discover_events['action'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the given data, we see that 39% of the events result in `open-book` event. It's hard to interpret this number without any context. Let's look at it further in multi-variate analysis. Similary, we can plot `event-type` and check its distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a27bc6d90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'event_type', data=discover_events)\n",
    "_, _ = plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate analysis\n",
    "\n",
    "\n",
    "Let's create two features to explore variables. Even though, there is not much information given about `arrival_timestamp` but lets see if we see any anomaly in the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# weekday\n",
    "discover_events['weekday'] = discover_events['dim_date_id'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# time difference in minutes\n",
    "discover_events['time_diff'] = abs((discover_events['arrival_timestamp'] \n",
    "                                      - discover_events['event_timestamp']).dt.total_seconds()).astype(int) / 60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `action` distribution by category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/bunnies/lib/python2.7/site-packages/seaborn/categorical.py:3692: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1180937d0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11768ba90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count action events for each category\n",
    "discover_events['flag']= 1\n",
    "\n",
    "action_by_cat = (pd.pivot_table(discover_events, \n",
    "               values = 'flag',\n",
    "               index= ['action'],\n",
    "               columns = ['category'],\n",
    "               aggfunc='sum',\n",
    "               fill_value=0)\n",
    "               .rename_axis(None, axis=1)\n",
    "               .reset_index())\n",
    "\n",
    "action_by_cat = (action_by_cat\n",
    "                 .set_index('action')\n",
    "                 .unstack()\n",
    "                 .reset_index())\n",
    "action_by_cat.columns = ['category','action','count']\n",
    "\n",
    "g = sns.catplot(x=\"action\", \n",
    "            y=\"count\", \n",
    "            hue=\"category\", \n",
    "            kind=\"point\", \n",
    "            data=action_by_cat,\n",
    "            size=7, aspect=2)\n",
    "g.set_xticklabels(rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice the following observations:\n",
    "\n",
    "1. Even though, search comprises of 70% events in the given data, it's `open-book` rate is almost half of `flex-discover` channel. \n",
    "2. However, for search, the `add-book` rate is higher than `flex-discover`. It means that, even though users are adding books from search, but they are not opening it to read. :(\n",
    "\n",
    "Let's see if this behaviour is any different if we group-by weekdays. This analysis would make more sense if we have more week-wise data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `action` distribution by weekday by category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x111438450>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118093750>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "events_by_cat_day = (pd.pivot_table(discover_events, \n",
    "               values = 'flag',\n",
    "               index= ['weekday','action'],\n",
    "               columns = ['category'],\n",
    "               aggfunc='sum',\n",
    "               fill_value=0)\n",
    "               .rename_axis(None, axis=1)\n",
    "               .reset_index())\n",
    "\n",
    "events_by_cat_day = events_by_cat_day.set_index(['weekday','action']).stack().reset_index()\n",
    "events_by_cat_day.columns = ['weekday','action','category','count']\n",
    "\n",
    "g = sns.catplot(x=\"action\", \n",
    "            y=\"count\", \n",
    "            hue=\"category\", \n",
    "            col = \"weekday\",\n",
    "            kind=\"bar\", \n",
    "            data=events_by_cat_day,\n",
    "            height=5, \n",
    "            aspect=2,\n",
    "            col_wrap=2,\n",
    "            col_order = [\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"])\n",
    "g.set_xticklabels(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't observe any significant difference in `add-book` and `open-book` events than what we saw above. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `event` distribution by category\n",
    "\n",
    "Since each event is defined category-wise, here we just see if the trackings are correct, we see as expected trend. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/bunnies/lib/python2.7/site-packages/seaborn/categorical.py:3692: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a299bbc50>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2198fdd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "discover_events['flag']= 1\n",
    "events_by_cat = (pd.pivot_table(discover_events, \n",
    "               values = 'flag',\n",
    "               index= ['event_type'],\n",
    "               columns = ['category'],\n",
    "               aggfunc='sum',\n",
    "               fill_value=0)\n",
    "               .rename_axis(None, axis=1)\n",
    "               .reset_index())\n",
    "\n",
    "events_by_cat = (events_by_cat\n",
    "                 .set_index('event_type')\n",
    "                 .unstack()\n",
    "                 .reset_index())\n",
    "events_by_cat.columns = ['category','event_type','count']\n",
    "\n",
    "g = sns.catplot(x=\"event_type\", \n",
    "            y=\"count\", \n",
    "            hue=\"category\", \n",
    "            kind=\"bar\", \n",
    "            data=events_by_cat,\n",
    "            size=7, aspect=2)\n",
    "g.set_xticklabels(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `time-diff` impact by category\n",
    "\n",
    "Let's see if the time-diff feature has any impact on search / discover behaviour of users. We are going to plot the average time-diff between categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x119fd4cd0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1fb7d410>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "action_time_by_cat = (pd.pivot_table(discover_events, \n",
    "               values = 'time_diff',\n",
    "               index= ['action'],\n",
    "               columns = ['category'],\n",
    "               aggfunc='mean',\n",
    "               fill_value=0)\n",
    "               .rename_axis(None, axis=1)\n",
    "               .reset_index())\n",
    "\n",
    "action_time_by_cat = (action_time_by_cat\n",
    "                 .set_index('action')\n",
    "                 .unstack()\n",
    "                 .reset_index())\n",
    "action_time_by_cat.columns = ['category','action','mean_time']\n",
    "\n",
    "g = sns.catplot(x=\"action\", \n",
    "            y=\"mean_time\", \n",
    "            hue=\"category\", \n",
    "            kind=\"point\", \n",
    "            data=action_time_by_cat,\n",
    "            height=7, aspect=2)\n",
    "g.set_xticklabels(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We learn the following things:\n",
    "1. The average time-diff for `flex-discover` is higher for `add-book` and `open-book` events.\n",
    "2. For all other events, the average time for `search` is significantly higher.\n",
    "\n",
    "We should gather more information on the significance of these time based variables and explore them further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insights\n",
    "\n",
    "Based on our analysis of the given dataset, we infer the following:\n",
    "\n",
    "**1. Short comings of RAKE Algorithm**\n",
    "\n",
    "RAKE algorithm selects keywords using a ratio of (degree of a keyword / frequency of a keyword). \n",
    "Based on the plot [above](#action-distribution-by-category), we see that keywords extracted using RAKE doesn't seem to be good representative of a book's context. Even though, users are adding books from `search` channel, they aren't reading them. \n",
    "\n",
    "**2. Improve search**\n",
    "\n",
    " * We should first understand how the current search is working. A good starting point is to do error-analysis. Take search sessions of users, analyse the search query and results given by the search engine. For example: if a user searched for `the alchemist` book but the results returned `the pilgrimage`, different book by the same author, users might add them but the search engine didn't do a job there. \n",
    " * Further research can be split into three phases:\n",
    "     * Analysing own data\n",
    "     * Analysing customer voice (complaints, requests)\n",
    "     * Analysing competitors platform\n",
    " * Analyse user search queries and figure out what do users look for (book title, book author, book year, book category) and use those meta-data information to match search queries instead of generated keywords.\n",
    " * Use a weighted function, something like `w1*book_title + w2*book_author + w3*book_keywords` to retrieve results for a given query.\n",
    " * Enable keyword search inside categories.\n",
    " * In case we don't have a exact match for a query (any book not yet available on blinkist), we can retreive similar searches based on vector representations of the user searches. \n",
    " * We can also enable intent based search backed by creating micro-categories / tags during books indexing time. For example: if a user searches for `motivational books`, it's a generic query, we could retreive results from category match for this query. Good example: [See here](https://www.amazon.com/Unfu-Yourself-Your-Head-into/dp/0062803832/ref=sr_1_1?keywords=motivational+books&qid=1562716998&s=gateway&sr=8-1)\n",
    " * Use special search based tools like apache-solr, elastic-search, we can leverage full text search features where we can index the entire book text in an optimized way and do a real time search on that.\n",
    " \n",
    "**3. Improve recommendation - Data points**\n",
    "\n",
    " * During signup flow, users are asked to select categories they would like. Using that information, could be a good starting point. \n",
    " * Using meta information from a book finished by user, it's author, year etc.\n",
    " * Each books as a feature image. We could use image features to recommend similar books. \n",
    " * Each book has a audio. We can use speech analysis for recommendations.\n",
    " * Users can also highlight / share text snippets in a book. This could be a good signal for search (point 2).\n",
    " \n",
    "**4. Recommendation Systems**\n",
    "\n",
    "There are multiple use-cases for recommendation systems:\n",
    "1. *Item Screen:* When a user is viewing a book page.\n",
    "2. *Home / Discover Screen:* When a user is exploring home screen or discover screen.\n",
    "3. *End Screen:* When a user finishes a book, what next ?\n",
    "\n",
    "Following are some ideas:\n",
    "  * *Generic recommendations*\n",
    "    * Popular books by category (based on # book adds)\n",
    "    * Popular books / authors during the week / month (based on # blinks)\n",
    "    * More by authors (of the book a user has already read)\n",
    "  * *Personalised recommmendations*\n",
    "    * People who read this also read ... (item co-occurence recommendations)\n",
    "    * Based on last book read / added, recommend similar books (search session based item embedding recommendation)"
   ]
  }
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