Answers.Body: <h3>The first step</h3>
  <p>You need to decide if you want to hold each string column or not. Then you must encode your text fields into numbers which you need to use some embedding algorithms like word2Vec. Check <a href="https://medium.com/sfu-cspmp/nlp-word-embedding-techniques-for-text-analysis-ec4e91bb886f" rel="nofollow noreferrer">here</a>.</p>
  <h3>Second step</h3>
  <p>Probably, you will have a lot of columns. Now, you need to reduce the space dimension. PCA, manifold transforms, partial least squares regression, etc., may help you in this way.</p>
  <h3>Third step</h3>
  <p>Here, you will have nice and tidy tabular data which you can feed into any Recommender System you want to.</p>
  <ol>
  <li><p>I presumed that you mean millions of columns when you say &quot;millions of fields&quot;.</p>
  </li>
  <li><p>If you mean millions of rows, thenprobably need to use methods thatdealingh Big Data.</p>
  </li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-10-12T19:32:54.710Z
Answers.LastActivityDate: 2022-07-13T12:41:37.850Z
Answers.LastEditDate: 2022-07-13T12:41:37.850Z
Answers.UserDisplayName: amin
Body: <p>I want to train a model based on millions of fields, including text and number, that are stored in a SQL database and recommend a perfect match based on some inputs. Now, which algorithm is the best for this problem?</p>
  <p>For instance, consider this database pattern:</p>
  <div class="s-table-container">
  <table class="s-table">
  <thead>
  <tr>
  <th>Title</th>
  <th>Content</th>
  <th>Volume</th>
  <th>Count</th>
  </tr>
  </thead>
  <tbody>
  <tr>
  <td>First</td>
  <td>row1</td>
  <td>5.36</td>
  <td>34</td>
  </tr>
  <tr>
  <td>Second</td>
  <td>row2</td>
  <td>36.1</td>
  <td>239</td>
  </tr>
  <tr>
  <td>...</td>
  <td>...</td>
  <td>...</td>
  <td>...</td>
  </tr>
  </tbody>
  </table>
  </div>
Tags: <machine-learning><natural-language-processing><recommender-system>
Title: A recommender system based on millions of fields including text and number
UserDisplayName: user197508
ThreadText: # A recommender system based on millions of fields including text and number
  
  (Asked by: username_0 on 2021-10-12)
  
  I want to train a model based on millions of fields, including text and number, that are stored in a SQL database and recommend a perfect match based on some inputs. Now, which algorithm is the best for this problem?
  
  For instance, consider this database pattern:
  
  | Title | Content | Volume | Count |
  | --- | --- | --- | --- |
  | First | row1 | 5.36 | 34 |
  | Second | row2 | 36.1 | 239 |
  | ... | ... | ... | ... |
  
  **Tags:** machine-learning, natural-language-processing, recommender-system
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-10-12. Score: 1
  
  ### The first step
  
  You need to decide if you want to hold each string column or not. Then you must encode your text fields into numbers which you need to use some embedding algorithms like word2Vec. Check [here](https://medium.com/sfu-cspmp/nlp-word-embedding-techniques-for-text-analysis-ec4e91bb886f).
  
  ### Second step
  
  Probably, you will have a lot of columns. Now, you need to reduce the space dimension. PCA, manifold transforms, partial least squares regression, etc., may help you in this way.
  
  ### Third step
  
  Here, you will have nice and tidy tabular data which you can feed into any Recommender System you want to.
  
  1. I presumed that you mean millions of columns when you say "millions of fields".
  2. If you mean millions of rows, thenprobably need to use methods thatdealingh Big Data.
  
  
  ---
  
  