  Matrix factorization is one of the most efficient approaches in recommender
systems. However, such algorithms, which rely on the interactions between users
and items, perform poorly for "cold-users" (users with little history of such
interactions) and at capturing the relationships between closely related items.
To address these problems, we propose a neural personalized embedding (NPE)
model, which improves the recommendation performance for cold-users and can
learn effective representations of items. It models a user's click to an item
in two terms: the personal preference of the user for the item, and the
relationships between this item and other items clicked by the user. We show
that NPE outperforms competing methods for top-N recommendations, specially for
cold-user recommendations. We also performed a qualitative analysis that shows
the effectiveness of the representations learned by the model.
