  Many Collaborative Filtering (CF) algorithms are item-based in the sense that
they analyze item-item relations in order to produce item similarities.
Recently, several works in the field of Natural Language Processing (NLP)
suggested to learn a latent representation of words using neural embedding
algorithms. Among them, the Skip-gram with Negative Sampling (SGNS), also known
as word2vec, was shown to provide state-of-the-art results on various
linguistics tasks. In this paper, we show that item-based CF can be cast in the
same framework of neural word embedding. Inspired by SGNS, we describe a method
we name item2vec for item-based CF that produces embedding for items in a
latent space. The method is capable of inferring item-item relations even when
user information is not available. We present experimental results that
demonstrate the effectiveness of the item2vec method and show it is competitive
with SVD.
