Category recommendation for users on an e-Commerce platform is an important task as it dictates the flow of traffic through the website. It is therefore important to surface precise and diverse category recommendations to aid the users' journey through the platform and to help them discover new groups of items. An often understated part in category recommendation is users' proclivity to repeat purchases. The structure of this temporal behavior can be harvested for better category recommendations and in this work, we attempt to harness this through variational inference. Further, to enhance the variational inference based optimization, we initialize the optimizer at better starting points through the well known Metapath2Vec algorithm. We demonstrate our results on two real-world datasets and show that our model outperforms standard baseline methods.
翻译:电子商业平台用户的分类建议是一项重要任务,因为它决定了通过网站的流量。 因此,必须提出准确和多样的类别建议,以帮助用户通过平台的行程,并帮助他们发现新的项目组。 类别建议中经常低估的部分是用户重复购买的倾向。 这种时间行为的结构可以通过更好的分类建议来收获,在这项工作中,我们试图通过变式推论来利用这一结构。 此外,为了加强基于变式推论的优化,我们通过众所周知的Metopath2Vec算法在更好的起点上开始优化优化。 我们在两个真实世界数据集上展示了我们的结果,并展示了我们的模型超过了标准基线方法。