Item2Vec: Neural Item Embedding for Collaborative Filtering

TL;DR

This paper introduces item2vec, a neural embedding approach inspired by word2vec, for item-based collaborative filtering that effectively captures item relations even without user data.

Contribution

The paper adapts the word2vec neural embedding technique to collaborative filtering, enabling item similarity inference without user information.

Findings

Item2Vec produces competitive item embeddings.

The method effectively captures item-item relations.

It performs well compared to SVD-based methods.

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.