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其下一步可能交互的物品。然而，用户交互可能会受到账户共享、不一致偏好或意外点击等噪声的影响。为解决这一问题，我们（i）提出了一种新的评估协议，该协议考虑了多个未来物品，并（ii）引入了一种新颖的、基于相关性的损失函数，用于训练能够处理多个未来物品的SRS，使其对噪声更具鲁棒性。我们的基于相关性的模型在传统评估协议下NDCG@10提升了约1.2%，HR提升了0.88%；而在新评估协议下，相对于最佳性能模型，NDCG@10提升了约1.63%，HR提升了约1.5%。