# Collaborative Translational Metric Learning

## TL;DR

This paper introduces TransCF, a novel metric learning approach for recommendation systems that models complex user-item relationships through translation vectors, improving top-N recommendation accuracy on real-world datasets.

## Contribution

It proposes a translation-based metric learning method that captures the heterogeneity of user-item interactions in implicit feedback, addressing limitations of previous single-point user representations.

## Key findings

- Outperforms state-of-the-art methods by up to 17% in hit ratio.
- Effectively models diverse user-item relationships with translation vectors.
- Provides qualitative insights into learned translation vectors.

## Abstract

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.