BiCoRec: Bias-Mitigated Context-Aware Sequential Recommendation Model

• URL: http://arxiv.org/abs/2512.13848v1
• Date: Mon, 15 Dec 2025 19:34:23 GMT
• Title: BiCoRec: Bias-Mitigated Context-Aware Sequential Recommendation Model
• Abstract summary: This study developed a novel framework, BiCoRec, that adaptively accommodates users changing preferences for popular and niche items.<n>BiCoRec achieves NDCG@10 scores of 0.0102, 0.0047, 0.0021, and 0.0005 for the Movies, Fashion, Games and Music datasets.
• Score: 1.0546092991752698
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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