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.

提出了一个新的评估协议来解决 “多余噪声” 对顺序推荐系统的影响，并引入了一种新型的关联感知损失函数来训练该系统，使其更具有鲁棒性，该模型在传统评估协议中 NDCG@10 有 0.88% 的提高，在新评估协议中有 1.63% 的 NDCG@10 和 1.5％的 HR 提高。