Contrastive Translation With Dynamical Temperature for Sequential Recommendation
针对传统对比学习破坏序列语义的问题，提出CT4Rec算法，通过向嵌入注入噪声生成增强视图，并采用动态温度策略提升推荐效果，在五个数据集上优于现有方法。
Contrastive learning is a promising solution to the problem of data sparsity in the field of recommendation system since it is able to extract self-supervised signals from raw data. The traditional contrastive learning-based sequential recommendation algorithms generate augmentations of original item sequences by utilizing crop, mask and reorder operations. However, those augmentation schemes destroy the underlying semantics of item sequences, resulting in difficulty in accurately defining positive and negative samples. To address this issue, we propose a contrastive translation based sequential recommendation algorithm, namely, CT4Rec. Specifically, CT4Rec generates augmented views of item sequences by injecting noises into embeddings of users and items, which is able to guarantee that the underlying semantics of augmented views are consistent with those of original item sequence. Hence, CT4Rec is able to effectively learn the invariances among the augmented views. In addition, the personalized translation operations are utilized to model the third-order relationships among entities. Moreover, it is difficult for contrastive learning-based recommendation algorithms with static temperature to simultaneously capture the differences among individual users/items and among the clusters of users/items. Hence, we utilize a dynamic temperature strategy to enhance CT4Rec, which endows CT4Rec with the capabilities of group-wise discrimination and instance discrimination. Our validation on five benchmark datasets shows that CT4Rec outperforms SOTA sequential recommendation methods. Our code is released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zar123123/CT4Rec</uri>.