  Most NN-RSs focus on accuracy by building representations from the direct
user-item interactions (e.g., user-item rating matrix), while ignoring the
underlying relatedness between users and items (e.g., users who rate the same
ratings for the same items should be embedded into similar representations),
which is an ideological disadvantage. On the other hand, ME models directly
employ inner products as a default loss function metric that cannot project
users and items into a proper latent space, which is a methodological
disadvantage. In this paper, we propose a supervised collaborative
representation learning model - Magnetic Metric Learning (MML) - to map users
and items into a unified latent vector space, enhancing the representation
learning for NN-RSs. Firstly, MML utilizes dual triplets to model not only the
observed relationships between users and items, but also the underlying
relationships between users as well as items to overcome the ideological
disadvantage. Specifically, a modified metric-based dual loss function is
proposed in MML to gather similar entities and disperse the dissimilar ones.
With MML, we can easily compare all the relationships (user to user, item to
item, user to item) according to the weighted metric, which overcomes the
methodological disadvantage. We conduct extensive experiments on four
real-world datasets with large item space. The results demonstrate that MML can
learn a proper unified latent space for representations from the user-item
matrix with high accuracy and effectiveness, and lead to a performance gain
over the state-of-the-art RS models by an average of 17%.
