# Sampler Design for Bayesian Personalized Ranking by Leveraging View Data

arXiv: https://arxiv.org/abs/1809.08162 · HTML: https://mlsanity.com/paper/1809.08162/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2018.

## Built on by (most cited first, 11 of 11)

- [Denoising Diffusion Recommender Model](https://mlsanity.com/paper/2401.06982/) (2024)
- [Bootstrapping User and Item Representations for One-Class Collaborative Filtering](https://mlsanity.com/paper/2105.06323/) (2021)
- [Efficient Bi-Level Optimization for Recommendation Denoising](https://mlsanity.com/paper/2210.10321/) (2022)
- [Double Correction Framework for Denoising Recommendation](https://mlsanity.com/paper/2405.11272/) (2024)
- [Learning Robust Recommenders through Cross-Model Agreement](https://mlsanity.com/paper/2105.09605/) (2021)
- [Scalable Personalised Item Ranking through Parametric Density Estimation](https://mlsanity.com/paper/2105.04769/) (2021)
- [Label Denoising through Cross-Model Agreement](https://mlsanity.com/paper/2308.13976/) (2023)
- [When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising Recommendation](https://mlsanity.com/paper/2409.12730/) (2024)
- [Learning Robust Recommenders from Noisy Implicit Feedback via GMM-Weighted Bayesian Transition Matrix](https://mlsanity.com/paper/2605.20721/) (2026)
- [ANCHOR: Agentic Noise Creation Framework for Human Simulation and Denoising Recommendation](https://mlsanity.com/paper/2606.05621/) (2026)
- [Denoising Implicit Feedback for Cold-start Recommendation](https://mlsanity.com/paper/2606.19658/) (2026)

## Abstract (arXiv)

Bayesian Personalized Ranking (BPR) is a representative pairwise learning method for optimizing recommendation models. It is widely known that the performance of BPR depends largely on the quality of negative sampler. In this paper, we make two contributions with respect to BPR. First, we find that sampling negative items from the whole space is unnecessary and may even degrade the performance. Second, focusing on the purchase feedback of E-commerce, we propose an effective sampler for BPR by leveraging the additional view data. In our proposed sampler, users' viewed interactions are considered as an intermediate feedback between those purchased and unobserved interactions. The pairwise rankings of user preference among these three types of interactions are jointly learned, and a user-oriented weighting strategy is considered during learning process, which is more effective and flexible. Compared to the vanilla BPR that applies a uniform sampler on all candidates, our view-enhanced sampler enhances BPR with a relative improvement over 37.03% and 16.40% on two real-world datasets. Our study demonstrates the importance of considering users' additional feedback when modeling their preference on different items, which avoids sampling negative items indiscriminately and inefficiently.