Sequential recommenders have been widely used in industry due to their
strength in modeling user preferences. While these models excel at learning a
user's positive interests, less attention has been paid to learning from
negative user feedback. Negative user feedback is an important lever of user
control, and comes with an expectation that recommenders should respond quickly
and reduce similar recommendations to the user. However, negative feedback
signals are often ignored in the training objective of sequential retrieval
models, which primarily aim at predicting positive user interactions. In this
work, we incorporate explicit and implicit negative user feedback into the
training objective of sequential recommenders in the retrieval stage using a
"not-to-recommend" loss function that optimizes for the log-likelihood of not
recommending items with negative feedback. We demonstrate the effectiveness of
this approach using live experiments on a large-scale industrial recommender
system. Furthermore, we address a challenge in measuring recommender
responsiveness to negative feedback by developing a counterfactual simulation
framework to compare recommender responses between different user actions,
showing improved responsiveness from the modeling change.

我们将负面用户反馈引入了序列推荐器的训练目标中，在检索阶段使用一种 “不推荐” 的损失函数来优化不推荐带有负面反馈的项目的对数似然。我们通过在大规模工业推荐系统上进行实时实验来证明了这种方法的有效性。此外，我们通过开发一个对照模拟框架来衡量推荐器对负面反馈的响应能力，展示了建模变化带来的响应能力改进。