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.

翻译：序列推荐模型旨在学习用户不断演变的偏好。然而，当前最先进的模型存在固有的流行度偏见。本研究开发了一种新颖的框架BiCoRec，能够自适应地适应用户对热门和冷门物品偏好的变化。我们的方法利用协同注意力机制获取流行度加权的用户序列表示，从而促进更准确的预测。随后，我们提出了一种新的训练方案，通过一致性损失函数从未来偏好中学习。BiCoRec旨在提升偏好冷门物品用户的推荐性能。对于这些用户，BiCoRec在NDCG@10指标上相较于最先进的基线模型实现了平均26.00%的提升。在将相关物品与整个集合进行排序时，BiCoRec在电影、时尚、游戏和音乐数据集上分别取得了0.0102、0.0047、0.0021和0.0005的NDCG@10分数。