论文标题
部分可观测时空混沌系统的无模型预测
Recommender Systems in E-commerce
论文作者
论文摘要
电子商务推荐系统在当前的数字世界中变得越来越重要。他们被用来个性化用户体验，帮助客户快速有效地找到所需的东西，并增加企业的收入。但是，基于大数据的电子商务推荐系统存在一些挑战。这些挑战包括资源有限，数据有效期，冷启动，长时间的问题，可扩展性。在本文中，我们讨论了克服这些挑战的挑战和潜在解决方案。我们还讨论了不同类型的电子商务推荐系统，其优势和缺点。我们以一些未来的研究指导结束，以提高电子商务推荐系统的性能。
E-commerce recommender systems are becoming increasingly important in the current digital world. They are used to personalize user experience, help customers find what they need quickly and efficiently, and increase revenue for the business. However, there are several challenges associated with big data-based e-commerce recommender systems. These challenges include limited resources, data validity period, cold start, long tail problem, scalability. In this paper, we discuss the challenges and potential solutions to overcome these challenges. We also discuss the different types of e-commerce recommender systems, their advantages, and disadvantages. We conclude with some future research directions to improve the performance of e-commerce recommender systems.
