# Multi-Perspective Attention Mechanism for Bias-Aware Sequential Recommendation

arXiv: https://arxiv.org/abs/2504.05323 · HTML: https://mlsanity.com/paper/2504.05323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 41 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation](https://mlsanity.com/paper/2305.00447/) (2023)
- [Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation](https://mlsanity.com/paper/2110.05730/) (2021)
- [Sequential Recommendation with Graph Neural Networks](https://mlsanity.com/paper/2106.14226/) (2021)
- [Debiased Contrastive Learning for Sequential Recommendation](https://mlsanity.com/paper/2303.11780/) (2023)
- [An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention](https://mlsanity.com/paper/2312.10325/) (2023)
- [LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems](https://mlsanity.com/paper/2411.01537/) (2024)
- [ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation](https://mlsanity.com/paper/2209.00456/) (2022)
- [Graph Masked Autoencoder for Sequential Recommendation](https://mlsanity.com/paper/2305.04619/) (2023)
- [TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems](https://mlsanity.com/paper/2308.14355/) (2023)
- [HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations](https://mlsanity.com/paper/2208.09586/) (2022)
- [Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation](https://mlsanity.com/paper/2304.14668/) (2023)
- [Variational Bandwidth Auto-encoder for Hybrid Recommender Systems](https://mlsanity.com/paper/2105.07597/) (2021)
- [Disentangled Variational Auto-encoder Enhanced by Counterfactual Data for Debiasing Recommendation](https://mlsanity.com/paper/2306.15961/) (2023)
- [Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation](https://mlsanity.com/paper/2308.03400/) (2023)

## Abstract (arXiv)

In the era of advancing information technology, recommender systems have emerged as crucial tools for dealing with information overload. However, traditional recommender systems still have limitations in capturing the dynamic evolution of user behavior. To better understand and predict user behavior, especially taking into account the complexity of temporal evolution, sequential recommender systems have gradually become the focus of research. Currently, many sequential recommendation algorithms ignore the amplification effects of prevalent biases, which leads to recommendation results being susceptible to the Matthew Effect. Additionally, it will impose limitations on the recommender system's ability to deeply perceive and capture the dynamic shifts in user preferences, thereby diminishing the extent of its recommendation reach. To address this issue effectively, we propose a recommendation system based on sequential information and attention mechanism called Multi-Perspective Attention Bias Sequential Recommendation (MABSRec). Firstly, we reconstruct user sequences into three short types and utilize graph neural networks for item weighting. Subsequently, an adaptive multi-bias perspective attention module is proposed to enhance the accuracy of recommendations. Experimental results show that the MABSRec model exhibits significant advantages in all evaluation metrics, demonstrating its excellent performance in the sequence recommendation task.