论文标题
产品推荐的对抗性学习
Adversarial learning for product recommendation
论文作者
论文摘要
产品推荐可以被视为数据融合中的问题 - 估计个人，行为以及利益的商品或服务之间的联合分布。这项工作提出了一个有条件的，耦合的生成对抗网络（推荐人），该网络学会了从极稀疏的隐式反馈培训数据中发现的（视图，买入）行为之间的联合分布中产生样本。用户互动由两个具有二进制值元素的矩阵表示。在每个矩阵中，非零值表示用户是否分别在给定产品类别中查看或购买了特定项目。通过以这种方式编码动作，该模型能够表示整个大型产品目录。在训练有素的GAN输出样本上计算出的转化率统计数据范围为1.323％至1.763％。与原假设检验结果相比，发现这些统计数据很重要。结果表明，与许多行业和产品类型中汇总的已发布的转换率相当。我们的结果是初步的，但是他们建议该模型提出的建议可能为消费者和数字零售商提供实用性。
Product recommendation can be considered as a problem in data fusion-- estimation of the joint distribution between individuals, their behaviors, and goods or services of interest. This work proposes a conditional, coupled generative adversarial network (RecommenderGAN) that learns to produce samples from a joint distribution between (view, buy) behaviors found in extremely sparse implicit feedback training data. User interaction is represented by two matrices having binary-valued elements. In each matrix, nonzero values indicate whether a user viewed or bought a specific item in a given product category, respectively. By encoding actions in this manner, the model is able to represent entire, large scale product catalogs. Conversion rate statistics computed on trained GAN output samples ranged from 1.323 to 1.763 percent. These statistics are found to be significant in comparison to null hypothesis testing results. The results are shown comparable to published conversion rates aggregated across many industries and product types. Our results are preliminary, however they suggest that the recommendations produced by the model may provide utility for consumers and digital retailers.
