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Collaborative filtering via euclidean embedding

Recommendation systems suggest items based on user preferences. Collaborative filtering is a popular approach in which recommending is based on the rating history of the system. One of the most accurate and scalable collaborative filtering algorithms is matrix factorization, which is based on a latent factor model. We propose a novel Euclidean embedding method as an alternative latent factor model to implement collaborative filtering. In this method, users and items are embedded in a unified Euclidean space where the distance between a user and an item is inversely proportional to the rating. This model is comparable to matrix factorization in terms of both scalability and accuracy while providing several advantages. First, the result of Euclidean embedding is more intuitively understandable for humans, allowing useful visualizations. Second, the neighborhood structure of the unified Euclidean space allows very efficient recommendation queries. Finally, the method facilitates online implementation requirements such as mapping new users or items in an existing model. Our experimental results confirm these advantages and show that collaborative filtering via Euclidean embedding is a promising approach for online recommender systems.