title: Can I use embedding layer instead of one hot as category input?
body: <p>I am trying to use FFM to predict binary labels. My dataset is as follows:</p>
  <pre><code>sex|age|price|label
  0|0|0|0
  1|0|1|1
  </code></pre>
  <p>I know that FFM is a model that consider some attributes as a same field. If I use one hot encoding to transform the dataset, then the dataset will looks like follows:</p>
  <pre><code>sex_0|sex_1|age_0|age_1|price_0|price_1|label
  0|0|0|0|0|0|0
  0|1|0|0|0|1|1
  </code></pre>
  <p>Thus, sex_0 and sex_1 can be considered as one field. The other attributes are similar.</p>
  <p>My question is whether can I use embedding layer to repalce the process of one hot encoding? However, this gives me some concerns.</p>
  <ol>
  <li>I have no any other related dataset, so I can not use any
  pre-trained embedding models. I can only randomly initialize the embedding
  weights and the train it by my own dataset. Will this way approach
  work?</li>
  <li>If I use embedding layer instead of one hot encoding, does it
  mean that each attribute will belongs one field?</li>
  <li>What is the difference between these two methods? Which is better?</li>
  </ol>