# Collaborative Similarity Embedding for Recommender Systems

## TL;DR

This paper introduces a unified framework called collaborative similarity embedding (CSE) that leverages both direct and implicit collaborative relations in user-item graphs to improve recommendation accuracy, especially in sparse data scenarios.

## Contribution

The paper proposes a novel CSE framework that differentiates and exploits direct and k-th order neighborhood relations for enhanced recommendation performance.

## Key findings

- CSE outperforms state-of-the-art methods on eight benchmark datasets.
- The sampling technique effectively captures different proximity relations.
- CSE improves recommendation accuracy in sparse graph scenarios.

## Abstract

We present collaborative similarity embedding (CSE), a unified framework that exploits comprehensive collaborative relations available in a user-item bipartite graph for representation learning and recommendation. In the proposed framework, we differentiate two types of proximity relations: direct proximity and k-th order neighborhood proximity. While learning from the former exploits direct user-item associations observable from the graph, learning from the latter makes use of implicit associations such as user-user similarities and item-item similarities, which can provide valuable information especially when the graph is sparse. Moreover, for improving scalability and flexibility, we propose a sampling technique that is specifically designed to capture the two types of proximity relations. Extensive experiments on eight benchmark datasets show that CSE yields significantly better performance than state-of-the-art recommendation methods.