中文标题#
通過對比對立的順序模式來受益於負面但有信息的反饋
英文标题#
Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential Patterns
中文摘要#
我們考慮在順序推薦場景中從正反饋和負反饋中學習的任務，因為這兩種類型的反饋通常存在於用戶互動中。 同時，傳統的順序學習模型通常專注於考慮和預測正向互動，而忽略了在推薦中減少具有負反饋的物品可以提高用戶對服務的滿意度。 此外，負反饋可能為更準確地識別真實用戶興趣提供有用的信號。 在本工作中，我們提出在單獨的正向和負向互動序列上訓練兩個 Transformer 編碼器。 我們使用一個包含正向和負向交叉熵以及一個巧妙設計的對比項的複合損失函數，將兩種類型的反饋納入順序推薦器的訓練目標中，這有助於更好地建模對立模式。 我們證明了這種方法的有效性，在增加真正正向指標方面優於最先進的順序推薦方法，同時減少了錯誤推廣的負向物品數量。
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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