Neural network approaches in recommender systems have shown remarkable
success by representing a large set of items as a learnable vector embedding
table. However, infrequent items may suffer from inadequate training
opportunities, making it difficult to learn meaningful representations. We
examine that in attribute and context-aware settings, the poorly learned
embeddings of infrequent items impair the recommendation accuracy. To address
such an issue, we propose a proxy-based item representation that allows each
item to be expressed as a weighted sum of learnable proxy embeddings. Here, the
proxy weight is determined by the attributes and context of each item and may
incorporate bias terms in case of frequent items to further reflect
collaborative signals. The proxy-based method calculates the item
representations compositionally, ensuring each representation resides inside a
well-trained simplex and, thus, acquires guaranteed quality. Additionally, that
the proxy embeddings are shared across all items allows the infrequent items to
borrow training signals of frequent items in a unified model structure and
end-to-end manner. Our proposed method is a plug-and-play model that can
replace the item encoding layer of any neural network-based recommendation
model, while consistently improving the recommendation performance with much
smaller parameter usage. Experiments conducted on real-world recommendation
benchmark datasets demonstrate that our proposed model outperforms
state-of-the-art models in terms of recommendation accuracy by up to 17% while
using only 10% of the parameters.

在推荐系统中，神经网络方法以可学习的向量嵌入表的形式表现大量条目，然而，在属性和上下文感知的设置中，不经常出现的条目可能会由于训练机会不足导致学习到的嵌入表示无意义，因此我们提出了一种基于代理的条目表示方法来解决这个问题，该方法通过代理权重根据每个条目的属性和上下文将条目表示为可学习的代理嵌入的加权求和，并且可以根据频繁出现的条目进一步反映合作信号，该方法通过组合计算条目表示，确保每个表示位于训练充分的单纯形内，从而获得保证质量，另外，代理嵌入在所有条目之间共享，使得不经常出现的条目能够在统一的模型结构和端到端的方式下借用频繁出现的条目的训练信号，我们的方法可以替换任何基于神经网络的推荐模型的条目编码层，同时在参数使用更小的情况下持续提高推荐性能，实验结果表明，我们的模型在实际推荐数据集上比现有模型提高了高达 17% 的推荐准确性，而只使用了 10% 的参数。

基于代理的属性和上下文感知推荐的物品表示

Proxy-based Item Representation for Attribute and Context-aware  Recommendation

Training a semantic segmentation model requires a large amount of pixel-level
annotation, hampering its application at scale. With computer graphics, we can
generate almost unlimited training data with precise annotation. However,a deep
model trained with synthetic data usually cannot directly generalize well to
realistic images due to domain shift. It has been observed that highly
confident labels for the unlabeled real images may be predicted relying on the
labeled synthetic data. To tackle the unsupervised domain adaptation problem,
we explore the possibilities to generate high-quality labels as proxy labels to
supervise the training on target data. Specifically, we propose a novel
proxy-based method using multi-adversarial training. We first train the model
using synthetic data (source domain). Multiple discriminators are used to align
the features be-tween the source and target domain (real images) at different
levels. Then we focus on obtaining and selecting high-quality proxy labels by
incorporating both the confidence of the class predictor and that from the
adversarial discriminators. Our discriminators not only work as a regularizer
to encourage feature alignment but also provide an alternative confidence
measure for generating proxy labels. Relying on the generated high-quality
proxies, our model can be trained in a "supervised manner" on the target
do-main. On two major tasks, GTA5->Cityscapes and SYNTHIA->Cityscapes, our
method achieves state-of-the-art results, outperforming the previous by a large
margin.

通过提出代理标签方法，使用多重对抗训练产生高质量标签，使得深度模型能够在目标域上进行带监督的训练，以解决语义分割过程中像素级别标注数量巨大，使其难以扩展。