Word Embedding techniques for Content-based Recommender Systems: An empirical evaluation

This work presents an empirical comparison among three widespread word embedding techniques as Latent Semantic Indexing, Random Indexing and the more recent Word2Vec. Specifically, we employed these techniques to learn a low-dimensional vector space word representation and we exploited it to represent both items and user profiles in a content-based recommendation scenario. The performance of the techniques has been evaluated against two state-of-the-art datasets, and experimental results provided good in-sights which pave the way to several future directions.