同时利用正负反馈提升推荐精准度，减少错误推荐。
Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential Patterns
- 用双Transformer分别处理正负交互序列，对比学习反对模式。
- 相比现有方法，真阳性指标提升，错推负反馈项目减少。
- 适合需要高精度推荐与用户满意度的场景，如电商、视频平台。
我们研究在序列推荐中同时学习正负反馈的任务，因为用户行为中常同时存在两类反馈。传统模型通常只关注正向交互预测，忽略了减少负反馈项目的推荐可提升用户满意度。此外，负反馈可能为更准确识别用户真实兴趣提供有用信号。本文提出使用两个Transformer编码器分别训练正负交互序列，并通过包含正负交叉熵及精心设计对比项的复合损失函数，融合两类反馈。该方法在真阳性指标上优于当前最优序列推荐模型，同时显著降低错误推荐负反馈项目数量。
原文摘要 · Abstract (English)
We consider the task of learning from both positive and negative feedback in a sequential recommendation scenario, as both types of feedback are often present in user interactions. Meanwhile, conventional sequential learning models usually focus on considering and predicting positive interactions, ignoring that reducing items with negative feedback in recommendations improves user satisfaction with the service. Moreover, the negative feedback can potentially provide a useful signal for more accurate identification of true user interests. In this work, we propose to train two transformer encoders on separate positive and negative interaction sequences. We incorporate both types of feedback into the training objective of the sequential recommender using a composite loss function that includes positive and negative cross-entropy as well as a cleverly crafted contrastive term, that helps better modeling opposing patterns. We demonstrate the effectiveness of this approach in terms of increasing true-positive metrics compared to state-of-the-art sequential recommendation methods while reducing the number of wrongly promoted negative items.
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