Learning from Negative User Feedback and Measuring Responsiveness for
  Sequential Recommenders
Yueqi Wang Yoni Halpern Shuo Chang Jingchen Feng Elaine Ya Le Longfei Li Xujian Liang Min-Cheng Huang Shane Li Alex Beutel Yaping Zhang Shuchao Bi
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.