多域环境中的建议：适应定制、Scalability和实时的性能
Recommendations in a Multi-Domain Setting: Adapting for Customization, Scalability and Real-Time Performance
摘要：
在ECIR'2022的这个行业演讲中，我们展示了如何构建一个现代推荐系统，该系统可以为各种应用领域提供实时的【形】推荐。具体来说，我们提出了我们的系统架构，该架构利用了文献中流行的推荐算法，如协同过滤、基于内容的过滤以及各种神经嵌入方法（例如，Doc2Vec、自动编码器等）。我们展示了我们的系统架构的适用性，使用两个现实世界的用例，即提供建议的域（i）就业市场，和（ii）创业启动成立。我们坚信，我们的经验，从研究和行业为导向的设置应该是感兴趣的从业者在实时的【形】多域推荐系统领域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
