Neural embeddings – not just for words
Word embedding technology has evolved in various ways since Word2Vec and GloVe. One such direction is the application of word embeddings to non-word settings, also known as neural embeddings. As you will recall, word embeddings leverage the distributional hypothesis that words occurring in similar contexts tend to have similar meanings, where context is usually a fixed-size (in number of words) window around the target word.
The idea of neural embeddings is very similar; that is, entities that occur in similar contexts tend to be strongly related to each other. The way in which these contexts are constructed is usually situation-dependent. We will describe two techniques here that are foundational and general enough to be applied easily to a variety of use cases.
Item2Vec
The Item2Vec embedding model was originally proposed by Barkan and Koenigstein [14] for the collaborative filtering use case, that is, recommending items...
