论文标题
多域设置中的建议：适应自定义，可伸缩性和实时性能
Recommendations in a Multi-Domain Setting: Adapting for Customization, Scalability and Real-Time Performance
论文作者
论文摘要
在ECIR'2022的这次行业演讲中，我们说明了如何构建现代推荐系统，该系统可以为各种应用程序领域实时提供建议。具体而言，我们介绍了我们的系统体系结构，该系统体系结构利用文献中的普遍建议算法，例如协作过滤，基于内容的过滤以及各种神经嵌入方法（例如，DOC2VEC，AutoCododers等）。我们使用两个现实世界的用例展示了系统体系结构的适用性，即为（i）就业市场和（ii）创业创业创建提供建议。我们坚信，我们来自研究和行业的环境的经验对于实时多域推荐系统领域的从业人员来说应该引起人们的兴趣。
In this industry talk at ECIR'2022, we illustrate how to build a modern recommender system that can serve recommendations in real-time for a diverse set of application domains. Specifically, we present our system architecture that utilizes popular recommendation algorithms from the literature such as Collaborative Filtering, Content-based Filtering as well as various neural embedding approaches (e.g., Doc2Vec, Autoencoders, etc.). We showcase the applicability of our system architecture using two real-world use-cases, namely providing recommendations for the domains of (i) job marketplaces, and (ii) entrepreneurial start-up founding. We strongly believe that our experiences from both research- and industry-oriented settings should be of interest for practitioners in the field of real-time multi-domain recommender systems.
