# BiCoRec: Bias-Mitigated Context-Aware Sequential Recommendation Model

arXiv: https://arxiv.org/abs/2512.13848 · HTML: https://mlsanity.com/paper/2512.13848/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 51 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 20 of 20)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://mlsanity.com/paper/2008.07873/) (2020)
- [RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms](https://mlsanity.com/paper/2011.01731/) (2020)
- [Towards Universal Sequence Representation Learning for Recommender Systems](https://mlsanity.com/paper/2206.05941/) (2022)
- [Diffusion Recommender Model](https://mlsanity.com/paper/2304.04971/) (2023)
- [Managing Popularity Bias in Recommender Systems with Personalized Re-ranking](https://mlsanity.com/paper/1901.07555/) (2019)
- [Translation-based Recommendation](https://mlsanity.com/paper/1707.02410/) (2017)
- [Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering](https://mlsanity.com/paper/1511.05234/) (2015)
- [Frequency Enhanced Hybrid Attention Network for Sequential Recommendation](https://mlsanity.com/paper/2304.09184/) (2023)
- [VIP5: Towards Multimodal Foundation Models for Recommendation](https://mlsanity.com/paper/2305.14302/) (2023)
- [Debiased Contrastive Learning for Sequential Recommendation](https://mlsanity.com/paper/2303.11780/) (2023)
- [ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance](https://mlsanity.com/paper/2005.10545/) (2020)
- [Multi-sided Exposure Bias in Recommendation](https://mlsanity.com/paper/2006.15772/) (2020)
- [Using Temporal Data for Making Recommendations](https://mlsanity.com/paper/1301.2320/) (2013)
- [Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce](https://mlsanity.com/paper/2110.11072/) (2021)
- [Multi-Modal Recommendation System with Auxiliary Information](https://mlsanity.com/paper/2210.10652/) (2022)

## Abstract (arXiv)

Sequential recommendation models aim to learn from users evolving preferences. However, current state-of-the-art models suffer from an inherent popularity bias. This study developed a novel framework, BiCoRec, that adaptively accommodates users changing preferences for popular and niche items. Our approach leverages a co-attention mechanism to obtain a popularity-weighted user sequence representation, facilitating more accurate predictions. We then present a new training scheme that learns from future preferences using a consistency loss function. BiCoRec aimed to improve the recommendation performance of users who preferred niche items. For these users, BiCoRec achieves a 26.00% average improvement in NDCG@10 over state-of-the-art baselines. When ranking the relevant item against the entire collection, BiCoRec achieves NDCG@10 scores of 0.0102, 0.0047, 0.0021, and 0.0005 for the Movies, Fashion, Games and Music datasets.