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。

音乐个性化的负反馈

Negative Feedback for Music Personalization

The graph-based recommendation has achieved great success in recent years.
However, most existing graph-based recommendations focus on capturing user
preference based on positive edges/feedback, while ignoring negative
edges/feedback (e.g., dislike, low rating) that widely exist in real-world
recommender systems. How to utilize negative feedback in graph-based
recommendations still remains underexplored. In this study, we first conducted
a comprehensive experimental analysis and found that (1) existing graph neural
networks are not well-suited for modeling negative feedback, which acts as a
high-frequency signal in a user-item graph. (2) The graph-based recommendation
suffers from the representation degeneration problem. Based on the two
observations, we propose a novel model that models positive and negative
feedback from a frequency filter perspective called Dual-frequency Graph Neural
Network for Sign-aware Recommendation (DFGNN). Specifically, in DFGNN, the
designed dual-frequency graph filter (DGF) captures both low-frequency and
high-frequency signals that contain positive and negative feedback.
Furthermore, the proposed signed graph regularization is applied to maintain
the user/item embedding uniform in the embedding space to alleviate the
representation degeneration problem. Additionally, we conduct extensive
experiments on real-world datasets and demonstrate the effectiveness of the
proposed model. Codes of our model will be released upon acceptance.

推荐系统中的负反馈在基于图的推荐中被广泛忽视，本研究提出了一种名为 DFGNN 的模型，通过设计双频图滤波器 (DGF) 来捕捉包含正反馈和负反馈的低频和高频信号，并应用有符号图正则化来解决表示退化问题。

DFGNN：双频图神经网络用于符号感知反馈

DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback

News recommendation is different from movie or e-commercial recommendation as
people usually do not grade the news. Therefore, user feedback for news is
always implicit (click behavior, reading time, etc). Inevitably, there are
noises in implicit feedback. On one hand, the user may exit immediately after
clicking the news as he dislikes the news content, leaving the noise in his
positive implicit feedback; on the other hand, the user may be recommended
multiple interesting news at the same time and only click one of them,
producing the noise in his negative implicit feedback. Opposite implicit
feedback could construct more integrated user preferences and help each other
to minimize the noise influence. Previous works on news recommendation only
used positive implicit feedback and suffered from the noise impact. In this
paper, we propose a denoising neural network for news recommendation with
positive and negative implicit feedback, named DRPN. DRPN utilizes both
feedback for recommendation with a module to denoise both positive and negative
implicit feedback to further enhance the performance. Experiments on the
real-world large-scale dataset demonstrate the state-of-the-art performance of
DRPN.

本文提出了一种名为 DRPN 的神经网络算法，利用正负隐式反馈来提高推荐性能，并通过降噪模块去除正负隐式反馈中的噪声，实现更好的新闻推荐性能。实验表明 DRPN 在大规模数据集上的表现是最先进的。