Next-item recommender systems are often trained using only positive feedback
with randomly-sampled negative feedback. We show the benefits of using real
negative feedback both as inputs into the user sequence and also as negative
targets for training a next-song recommender system for internet radio. In
particular, using explicit negative samples during training helps reduce
training time by ~60% while also improving test accuracy by ~6%; adding user
skips as additional inputs also can considerably increase user coverage
alongside slightly improving accuracy. We test the impact of using a large
number of random negative samples to capture a 'harder' one and find that the
test accuracy increases with more randomly-sampled negatives, but only to a
point. Too many random negatives leads to false negatives that limits the lift,
which is still lower than if using true negative feedback. We also find that
the test accuracy is fairly robust with respect to the proportion of different
feedback types, and compare the learned embeddings for different feedback
types.

使用真实的负反馈作为输入和训练目标，在网络广播的下一首推荐系统中，有助于减少训练时间约 60％，并提高测试准确性约 6％；同时，添加用户跳过作为额外输入还可以显著增加用户覆盖率并略微提高准确性。