Recommendation techniques are important approaches for alleviating
information overload. Being often trained on implicit user feedback, many
recommenders suffer from the sparsity challenge due to the lack of explicitly
negative samples. The GAN-style recommenders (i.e., IRGAN) addresses the
challenge by learning a generator and a discriminator adversarially, such that
the generator produces increasingly difficult samples for the discriminator to
accelerate optimizing the discrimination objective. However, producing samples
from the generator is very time-consuming, and our empirical study shows that
the discriminator performs poor in top-k item recommendation. To this end, a
theoretical analysis is made for the GAN-style algorithms, showing that the
generator of limit capacity is diverged from the optimal generator. This may
interpret the limitation of discriminator's performance. Based on these
findings, we propose a Sampling-Decomposable Generative Adversarial Recommender
(SD-GAR). In the framework, the divergence between some generator and the
optimum is compensated by self-normalized importance sampling; the efficiency
of sample generation is improved with a sampling-decomposable generator, such
that each sample can be generated in O(1) with the Vose-Alias method.
Interestingly, due to decomposability of sampling, the generator can be
optimized with the closed-form solutions in an alternating manner, being
different from policy gradient in the GAN-style algorithms. We extensively
evaluate the proposed algorithm with five real-world recommendation datasets.
The results show that SD-GAR outperforms IRGAN by 12.4% and the SOTA
recommender by 10% on average. Moreover, discriminator training can be 20x
faster on the dataset with more than 120K items.

这篇论文提出了一种采样分解生成对抗式推荐器 (SD-GAR) 方法，旨在解决生成样本耗时以及推荐过程中负样本缺乏的问题。作者通过自规范化重要性采样以及分解采样来解决推荐中的挑战，并在五个真实的推荐数据集上进行了评估，结果显示 SD-GAR 的推荐效果比目前最先进的方法还要好。