Sequential Recommender Systems (SRSs) are a popular type of recommender system that learns from a user's history to predict the next item they are likely to interact with. However, user interactions can be affected by noise stemming from account sharing, inconsistent preferences, or accidental clicks. To address this issue, we (i) propose a new evaluation protocol that takes multiple future items into account and (ii) introduce a novel relevance-aware loss function to train a SRS with multiple future items to make it more robust to noise. Our relevance-aware models obtain an improvement of ~1.2% of NDCG@10 and 0.88% in the traditional evaluation protocol, while in the new evaluation protocol, the improvement is ~1.63% of NDCG@10 and ~1.5% of HR w.r.t the best performing models.

翻译：序列推荐系统（SRSs）是一种流行的推荐系统类型，它通过学习用户的历史行为来预测用户下一个可能交互的物品。然而，用户交互可能受到账户共享、不一致偏好或误点击等噪声的影响。为解决此问题，我们（i）提出了一种新的评估协议，该协议考虑多个未来物品，并（ii）引入了一种新颖的相关性感知损失函数，利用多个未来物品训练SRS，使其对噪声更具鲁棒性。我们的相关性感知模型在传统评估协议下，NDCG@10提升了约1.2%，总体指标提升了0.88%；而在新评估协议下，相较于表现最佳的模型，NDCG@10提升了约1.63%，命中率（HR）提升了约1.5%。