Item2Vec: Neural Item Embedding for Collaborative Filtering

Understand

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.