解决推荐系统对热门商品的偏见，提升小众商品推荐效果
BiCoRec: Bias-Mitigated Context-Aware Sequential Recommendation Model
- 用共注意力机制融合用户偏好与商品流行度，动态建模序列行为
- 引入一致性损失函数，利用未来偏好优化当前预测，提升准确性
- 特别改善冷门商品推荐，适合关注长尾内容的场景
序列推荐模型旨在捕捉用户偏好演化，但现有先进模型存在固有的热门偏见。本文提出BiCoRec框架，自适应地建模用户对热门与小众商品偏好的变化。通过共注意力机制生成流行度加权的用户序列表示，实现更精准预测。同时设计一种新训练方案，利用一致性损失函数从未来偏好中学习。该模型显著提升了偏好小众商品用户的推荐性能：在主流基准上，NDCG@10平均提升26.00%。在Movies、Fashion、Games和Music数据集上，其NDCG@10分别为0.0102、0.0047、0.0021和0.0005。
原文摘要 · Abstract (English)
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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