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。

可分解的生成对抗推荐采样

Sampling-Decomposable Generative Adversarial Recommender

Online ranker evaluation is a key challenge in information retrieval. An
important task in the online evaluation of rankers is using implicit user
feedback for inferring preferences between rankers. Interleaving methods have
been found to be efficient and sensitive, i.e. they can quickly detect even
small differences in quality. It has recently been shown that multileaving
methods exhibit similar sensitivity but can be more efficient than interleaving
methods. This paper presents empirical results demonstrating that existing
multileaving methods either do not scale well with the number of rankers, or,
more problematically, can produce results which substantially differ from
evaluation measures like NDCG. The latter problem is caused by the fact that
they do not correctly account for the similarities that can occur between
rankers being multileaved. We propose a new multileaving method for handling
this problem and demonstrate that it substantially outperforms existing
methods, in some cases reducing errors by as much as 50%.

本研究提出新的多重交错方法以更好地评估线上排名算法，并在实证实验中证明，该方法相较于现有方法可显著减小误差，最高可降低 50%。