# Improving Micro-video Recommendation by Controlling Position Bias

arXiv: https://arxiv.org/abs/2208.05315 · HTML: https://mlsanity.com/paper/2208.05315/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2022.

## Built on by (most cited first, 5 of 5)

- [Denoising Long- and Short-term Interests for Sequential Recommendation](https://mlsanity.com/paper/2407.14743/) (2024)
- [Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations](https://mlsanity.com/paper/2508.09789/) (2025)
- [Adaptively Learning to Select-Rank in Online Platforms](https://mlsanity.com/paper/2406.05017/) (2024)
- [Orthogonal Hyper-category Guided Multi-interest Elicitation for Micro-video Matching](https://mlsanity.com/paper/2407.14741/) (2024)
- [Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation](https://mlsanity.com/paper/2605.08810/) (2026)

## Abstract (arXiv)

As the micro-video apps become popular, the numbers of micro-videos and users increase rapidly, which highlights the importance of micro-video recommendation. Although the micro-video recommendation can be naturally treated as the sequential recommendation, the previous sequential recommendation models do not fully consider the characteristics of micro-video apps, and in their inductive biases, the role of positions is not in accord with the reality in the micro-video scenario. Therefore, in the paper, we present a model named PDMRec (Position Decoupled Micro-video Recommendation). PDMRec applies separate self-attention modules to model micro-video information and the positional information and then aggregate them together, avoid the noisy correlations between micro-video semantics and positional information being encoded into the sequence embeddings. Moreover, PDMRec proposes contrastive learning strategies which closely match with the characteristics of the micro-video scenario, thus reducing the interference from micro-video positions in sequences. We conduct the extensive experiments on two real-world datasets. The experimental results shows that PDMRec outperforms existing multiple state-of-the-art models and achieves significant performance improvements.