# Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking

arXiv: https://arxiv.org/abs/1707.05176 · HTML: https://mlsanity.com/paper/1707.05176/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2017.

## Built on by (most cited first, 30 of 39)

- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [How Powerful is Graph Convolution for Recommendation?](https://mlsanity.com/paper/2108.07567/) (2021)
- [Sequential Recommendation via Stochastic Self-Attention](https://mlsanity.com/paper/2201.06035/) (2022)
- [Blurring-Sharpening Process Models for Collaborative Filtering](https://mlsanity.com/paper/2211.09324/) (2022)
- [HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization](https://mlsanity.com/paper/2204.08176/) (2022)
- [Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network](https://mlsanity.com/paper/2110.04002/) (2021)
- [Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems](https://mlsanity.com/paper/2105.08908/) (2021)
- [HICF: Hyperbolic Informative Collaborative Filtering](https://mlsanity.com/paper/2207.09051/) (2022)
- [Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation](https://mlsanity.com/paper/2106.06165/) (2021)
- [Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling](https://mlsanity.com/paper/2206.11549/) (2022)
- [Attention Mixtures for Time-Aware Sequential Recommendation](https://mlsanity.com/paper/2304.08158/) (2023)
- [Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations](https://mlsanity.com/paper/2104.08490/) (2021)
- [Collaboration-Aware Graph Convolutional Network for Recommender Systems](https://mlsanity.com/paper/2207.06221/) (2022)
- [Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation](https://mlsanity.com/paper/2101.04849/) (2021)
- [The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm](https://mlsanity.com/paper/2209.15292/) (2022)
- [Improved Diversity-Promoting Collaborative Metric Learning for Recommendation](https://mlsanity.com/paper/2409.01012/) (2024)
- [PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation](https://mlsanity.com/paper/2411.00163/) (2024)
- [A Fashion Item Recommendation Model in Hyperbolic Space](https://mlsanity.com/paper/2409.02599/) (2024)
- [Hierarchical Latent Relation Modeling for Collaborative Metric Learning](https://mlsanity.com/paper/2108.04655/) (2021)
- [Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation](https://mlsanity.com/paper/2109.02915/) (2021)
- [Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering](https://mlsanity.com/paper/2202.13140/) (2022)
- [Augmenting the User-Item Graph with Textual Similarity Models](https://mlsanity.com/paper/2109.09358/) (2021)
- [Pre-trained Neural Recommenders: A Transferable Zero-Shot Framework for Recommendation Systems](https://mlsanity.com/paper/2309.01188/) (2023)
- [IA-GCN: Interactive Graph Convolutional Network for Recommendation](https://mlsanity.com/paper/2204.03827/) (2022)
- [Neuro-Symbolic Recommendation Model based on Logic Query](https://mlsanity.com/paper/2309.07594/) (2023)
- [Advancing Loss Functions in Recommender Systems: A Comparative Study with a R'enyi Divergence-Based Solution](https://mlsanity.com/paper/2506.15120/) (2025)
- [Learning Complex Users' Preferences for Recommender Systems](https://mlsanity.com/paper/2107.01529/) (2021)
- [A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data](https://mlsanity.com/paper/2204.07819/) (2022)
- [Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders](https://mlsanity.com/paper/2308.06885/) (2023)
- [ShufaNet: Classification method for calligraphers who have reached the professional level](https://mlsanity.com/paper/2111.11350/) (2021)

## Abstract (arXiv)

This paper proposes a new neural architecture for collaborative ranking with implicit feedback. Our model, LRML (textitLatent Relational Metric Learning) is a novel metric learning approach for recommendation. More specifically, instead of simple push-pull mechanisms between user and item pairs, we propose to learn latent relations that describe each user item interaction. This helps to alleviate the potential geometric inflexibility of existing metric learing approaches. This enables not only better performance but also a greater extent of modeling capability, allowing our model to scale to a larger number of interactions. In order to do so, we employ a augmented memory module and learn to attend over these memory blocks to construct latent relations. The memory-based attention module is controlled by the user-item interaction, making the learned relation vector specific to each user-item pair. Hence, this can be interpreted as learning an exclusive and optimal relational translation for each user-item interaction. The proposed architecture demonstrates the state-of-the-art performance across multiple recommendation benchmarks. LRML outperforms other metric learning models by 6%-7.5% in terms of Hits@10 and nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover, qualitative studies also demonstrate evidence that our proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback. As such, this ascertains the ability of LRML to uncover hidden relational structure within implicit datasets.