@Item2Vec: Neural Item Embedding for Collaborative Filtering
[[Abstract]]
-
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.
[[Attachments]]
@Item2Vec: Neural Item Embedding for Collaborative Filtering