# Collaborative Similarity Embedding for Recommender Systems

arXiv: https://arxiv.org/abs/1902.06188 · HTML: https://mlsanity.com/paper/1902.06188/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2019.

## Built on by (most cited first, 13 of 13)

- [Graph Learning based Recommender Systems: A Review](https://mlsanity.com/paper/2105.06339/) (2021)
- [MMREC: LLM Based Multi-Modal Recommender System](https://mlsanity.com/paper/2408.04211/) (2024)
- [Embedding in Recommender Systems: A Survey](https://mlsanity.com/paper/2310.18608/) (2023)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [SiReN: Sign-Aware Recommendation Using Graph Neural Networks](https://mlsanity.com/paper/2108.08735/) (2021)
- [Instant Representation Learning for Recommendation over Large Dynamic Graphs](https://mlsanity.com/paper/2305.18622/) (2023)
- [DLRREC: Denoising Latent Representations via Multi-Modal Knowledge Fusion in Deep Recommender Systems](https://mlsanity.com/paper/2512.00596/) (2025)
- [Combining Embedding-Based and Semantic-Based Models for Post-hoc Explanations in Recommender Systems](https://mlsanity.com/paper/2401.04474/) (2024)
- [HybridGNN: Learning Hybrid Representation in Multiplex Heterogeneous Networks](https://mlsanity.com/paper/2208.02068/) (2022)
- [Semantic Item Graph Enhancement for Multimodal Recommendation](https://mlsanity.com/paper/2508.06154/) (2025)
- [Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization](https://mlsanity.com/paper/2110.06117/) (2021)
- [Online and Offline Evaluations of Collaborative Filtering and Content Based Recommender Systems](https://mlsanity.com/paper/2411.01354/) (2024)

## Abstract (arXiv)

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.