<p>You should look into <strong>entity embedding</strong> if you are searching for a way to utilize embeddings for categorical variables.</p>

<ul>
<li>google has a good crash course on the topic: <a href="https://developers.google.com/machine-learning/crash-course/embeddings/categorical-input-data" rel="nofollow noreferrer">https://developers.google.com/machine-learning/crash-course/embeddings/categorical-input-data</a></li>
<li>this is a good paper on arxiv written by a team from a Kaggle competition: <a href="https://arxiv.org/abs/1604.06737" rel="nofollow noreferrer">https://arxiv.org/abs/1604.06737</a></li>
</ul>
Is it a good idea to use word2vec for encoding of categorical features?
<p>I am facing a binary prediction task and have a set of features of which all are categorical. A key challenge is therefore to encode those categorical features to numbers and I was looking for smart ways to do so. 
I stumbled over word2vec, which is mostly used for NLP, but I was wondering whether I could use it to encode my variables, i.e. simply take the weights of the neural net as the encoded features.</p>

<p>However, I am not sure, whether it is a good idea since, the context words, which serve as the input features in word2vec are in my case more or less random, in contrast to real sentences which word2vec was originially made for. </p>

<p>Do you guys have any advice, thoughts, recommendations on this?</p>