This document discusses Item2Vec, a content-based item recommender system that uses similarity-based recommendations. It trains embeddings for items and their contexts using customer click sequence data to learn relationships between items. These embeddings are then used in a logistic regression model to perform binary classification to predict whether a target item and context item co-occur based on if they are a positive or negative sample.