NPE: Neural Personalized Embedding for Collaborative Filtering

NPE: Neural Personalized Embedding for Collaborative Filtering

ThaiBinh Nguyen, Atsuhiro Takasu

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 1583-1589. https://doi.org/10.24963/ijcai.2018/219

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.
Keywords:
Machine Learning: Data Mining
Machine Learning: Neural Networks
Humans and AI: Personalization and User Modeling
Machine Learning: Recommender Systems
Multidisciplinary Topics and Applications: Information Retrieval