中文标题#
通过对比对立的顺序模式来受益于负面但有信息的反馈
英文标题#
Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential Patterns
中文摘要#
我们考虑在顺序推荐场景中从正反馈和负反馈中学习的任务，因为这两种类型的反馈通常存在于用户交互中。 同时，传统的顺序学习模型通常专注于考虑和预测正向交互，而忽略了在推荐中减少具有负反馈的物品可以提高用户对服务的满意度。 此外，负反馈可能为更准确地识别真实用户兴趣提供有用的信号。 在本工作中，我们提出在单独的正向和负向交互序列上训练两个 Transformer 编码器。 我们使用一个包含正向和负向交叉熵以及一个巧妙设计的对比项的复合损失函数，将两种类型的反馈纳入顺序推荐器的训练目标中，这有助于更好地建模对立模式。 我们证明了这种方法的有效性，在增加真正正向指标方面优于最先进的顺序推荐方法，同时减少了错误推广的负向物品数量。
英文摘要#
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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