NPE: Neural Personalized Embedding for Collaborative Filtering
This addresses a specific bottleneck in recommender systems for users with limited interaction history, representing an incremental improvement over matrix factorization methods.
The paper tackles the problem of poor recommendation performance for cold-users and capturing item relationships in collaborative filtering by proposing a neural personalized embedding (NPE) model, which outperforms competing methods for top-N recommendations, especially for cold-users.
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.