  Recently, matrix factorization-based recommendation methods have been
criticized for the problem raised by the triangle inequality violation.
Although several metric learning-based approaches have been proposed to
overcome this issue, existing approaches typically project each user to a
single point in the metric space, and thus do not suffice for properly modeling
the intensity and the heterogeneity of user-item relationships in implicit
feedback. In this paper, we propose TransCF to discover such latent user-item
relationships embodied in implicit user-item interactions. Inspired by the
translation mechanism popularized by knowledge graph embedding, we construct
user-item specific translation vectors by employing the neighborhood
information of users and items, and translate each user toward items according
to the user's relationships with the items. Our proposed method outperforms
several state-of-the-art methods for top-N recommendation on seven real-world
data by up to 17% in terms of hit ratio. We also conduct extensive qualitative
evaluations on the translation vectors learned by our proposed method to
ascertain the benefit of adopting the translation mechanism for implicit
feedback-based recommendations.
