# Scalable Personalised Item Ranking through Parametric Density Estimation

arXiv: https://arxiv.org/abs/2105.04769 · HTML: https://mlsanity.com/paper/2105.04769/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 74 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 30 of 31)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Wasserstein Auto-Encoders](https://mlsanity.com/paper/1711.01558/) (2017)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models](https://mlsanity.com/paper/1705.10513/) (2017)
- [Modeling User Exposure in Recommendation](https://mlsanity.com/paper/1510.07025/) (2015)
- [RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback](https://mlsanity.com/paper/1912.11160/) (2019)
- [Density Estimation in Infinite Dimensional Exponential Families](https://mlsanity.com/paper/1312.3516/) (2013)
- [Reinforced Negative Sampling over Knowledge Graph for Recommendation](https://mlsanity.com/paper/2003.05753/) (2020)
- [Adaptive Sampled Softmax with Kernel Based Sampling](https://mlsanity.com/paper/1712.00527/) (2017)
- [Adversarial Example Games](https://mlsanity.com/paper/2007.00720/) (2020)
- [Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering](https://mlsanity.com/paper/2009.03376/) (2020)
- [Exponential Family Estimation via Adversarial Dynamics Embedding](https://mlsanity.com/paper/1904.12083/) (2019)
- [Deep Adversarial Social Recommendation](https://mlsanity.com/paper/1905.13160/) (2019)
- [Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics](https://mlsanity.com/paper/1501.02629/) (2015)
- [Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback](https://mlsanity.com/paper/2003.01892/) (2020)
- [Kernel Exponential Family Estimation via Doubly Dual Embedding](https://mlsanity.com/paper/1811.02228/) (2018)
- [A Convex Duality Framework for GANs](https://mlsanity.com/paper/1810.11740/) (2018)
- [Fast Matrix Factorization with Non-Uniform Weights on Missing Data](https://mlsanity.com/paper/1811.04411/) (2018)
- [Sampler Design for Bayesian Personalized Ranking by Leveraging View Data](https://mlsanity.com/paper/1809.08162/) (2018)
- [Kernel Conditional Exponential Family](https://mlsanity.com/paper/1711.05363/) (2017)
- [Density-Ratio Based Personalised Ranking from Implicit Feedback](https://mlsanity.com/paper/2101.07481/) (2021)
- [Wasserstein Collaborative Filtering for Item Cold-start Recommendation](https://mlsanity.com/paper/1909.04266/) (2019)
- [Wasserstein Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1809.05662/) (2018)

## Abstract (arXiv)

Learning from implicit feedback is challenging because of the difficult nature of the one-class problem: we can observe only positive examples. Most conventional methods use a pairwise ranking approach and negative samplers to cope with the one-class problem. However, such methods have two main drawbacks particularly in large-scale applications; (1) the pairwise approach is severely inefficient due to the quadratic computational cost; and (2) even recent model-based samplers (e.g. IRGAN) cannot achieve practical efficiency due to the training of an extra model. In this paper, we propose a learning-to-rank approach, which achieves convergence speed comparable to the pointwise counterpart while performing similarly to the pairwise counterpart in terms of ranking effectiveness. Our approach estimates the probability densities of positive items for each user within a rich class of distributions, viz. exponential family. In our formulation, we derive a loss function and the appropriate negative sampling distribution based on maximum likelihood estimation. We also develop a practical technique for risk approximation and a regularisation scheme. We then discuss that our single-model approach is equivalent to an IRGAN variant under a certain condition. Through experiments on real-world datasets, our approach outperforms the pointwise and pairwise counterparts in terms of effectiveness and efficiency.