Following data ethics and respecting the clients’ privacy, the banking environment can use the client data that is available to them to offer personalized services to its clients. Intelligent recommender systems can support this attempt through specialized technological architectures. This article proposes the inclusion of CEBRA (CasE-Based Reasoning Application), a case-based reasoning system oriented to commercial banking, in a Fog Computing architecture coordinated by virtual agents. Throughout this article, the model of this architecture is presented and its life cycle is described, and improvements are proposed through the incorporation of several techniques in the retrieve and reuse phases, including the extraction of interests expressed by users on their social network profiles and collaborative filtering systems. A comprehensive case study has been carried out and a dataset of 60,000 cases has been generated to evaluate CEBRA. As a result, the Recommender System is presented, by including, the recommendation algorithm and a REST interface for its use. The recommendations are based on the user’s profile, previous ratings and/or additional knowledge such as the user’s contextual information. The proposal takes advantage of contextual information to support the promotion of banking and financial products, improving user satisfaction.

遵循数据道德并尊重客户隐私，银行环境可以使用他们可用的客户数据为其客户提供个性化服务。智能推荐系统可以通过专门的技术架构来支持这种尝试。本文建议将 CEBRA（CasE-Based Reasoning Application），一种面向商业银行的基于案例的推理系统，包含在由虚拟代理协调的雾计算架构中。在整篇文章中，介绍了该架构的模型并描述了其生命周期，并通过在检索和重用阶段结合多种技术提出了改进建议，包括提取用户在其社交网络配置文件中表达的兴趣和协作过滤系统。已经进行了全面的案例研究，并生成了 60,000 个案例的数据集来评估 CEBRA。因此，通过包含推荐算法和供其使用的 REST 接口来呈现推荐系统。推荐基于用户的个人资料、以前的评级和/或附加知识，例如用户的上下文信息。该提案利用上下文信息来支持银行和金融产品的推广，提高用户满意度。以前的评级和/或附加知识，例如用户的上下文信息。该提案利用上下文信息来支持银行和金融产品的推广，提高用户满意度。以前的评级和/或附加知识，例如用户的上下文信息。该提案利用上下文信息来支持银行和金融产品的推广，提高用户满意度。