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Let’s say that you’re working on a job recommendation engine. You have access to all user Linkedin profiles, a list of jobs each user applied to, and answers to questions that the user filled in about their job search.

Using this information, how would you build a job recommendation feed?

What problem can arise in job recommendation systems with regard to initial recommendations?

What problem can arise in job recommendation systems with regard to initial recommendations?

Job Recommendation

Write a function <code>get_ngrams</code> to take in a word (string) and return a dictionary of n-grams and their frequency in the given string.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>string = &#39;banana&#39; 
n=2
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>output = {&#39;ba&#39;:1, &#39;an&#39;:2, &#39;na&#39;:2} 
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>string = &#39;banana&#39; 
n=3 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>output = {&#39;ban&#39;:1, &#39;ana&#39;:2, &#39;nan&#39;:1}
</code></pre>

N-gram Dictionary

Write a SQL query to create a histogram of the number of comments per user in the month of January 2020.

Note: Assume bin buckets class intervals of one.

Note: Comments that were created outside of January 2020 should be counted in a “0” bucket

Example:

Input:

<code>users</code> table

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>neighborhood_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>mail</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<code>comments</code> table

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>body</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>comment_count</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>frequency</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Write a SQL query to create a histogram of the number of comments per user in the month of January 2020.

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