论文标题
多模式嵌入基于融合的建议剂
Multi-modal Embedding Fusion-based Recommender
论文作者
论文摘要
推荐系统最近在全球范围内广受欢迎，在线交互系统中具有主要用例，重点关注电子商务平台。我们已经开发了一个基于机器学习的推荐平台，该平台几乎可以轻松地应用于任何项目和/或操作域。与现有的推荐系统相反，我们的平台支持多种类型的交互数据，其本质上具有多种元数据。这是通过各种数据表示的多模式融合来实现的。我们将平台部署到多家不同类型的电子商务商店中，例如食品和饮料，鞋子，时尚用品，电信运营商。在这里，我们介绍我们的系统，其灵活性和性能。我们还在“开放数据集”上显示基准结果，这显着胜过先前的最先进的工作。
Recommendation systems have lately been popularized globally, with primary use cases in online interaction systems, with significant focus on e-commerce platforms. We have developed a machine learning-based recommendation platform, which can be easily applied to almost any items and/or actions domain. Contrary to existing recommendation systems, our platform supports multiple types of interaction data with multiple modalities of metadata natively. This is achieved through multi-modal fusion of various data representations. We deployed the platform into multiple e-commerce stores of different kinds, e.g. food and beverages, shoes, fashion items, telecom operators. Here, we present our system, its flexibility and performance. We also show benchmark results on open datasets, that significantly outperform state-of-the-art prior work.
