This paper presents a Multi-Agent approach to the problem of recommending training courses to engineering professionals. The recommendation system is built as a proof of concept and limited to the electrical and mechanical engineering disciplines. Through user modelling and data collection from a survey, collaborative filtering recommendation is implemented using intelligent agents. The agents work together in recommending meaningful training courses and updating the course information. The system uses a users profile and keywords from courses to rank courses. A ranking accuracy for courses of 90% is achieved while flexibility is achieved using an agent that retrieves information autonomously using data mining techniques from websites. This manner of recommendation is scalable and adaptable. Further improvements can be made using clustering and recording user feedback.
GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems
City University of Hong Kong（香港城市大学）；High Magnetic Field Laboratory, Chinese Academy of Sciences（中国科学院强磁场科学中心）；Southern University of Science and Technology（南方科技大学）；Jinan University（暨南大学）；University of Science and Technology of China（中国科学技术大学）
Intelligent Request Strategy Design in Recommender System
Alibaba Group（阿里巴巴集团）；Institute of Artificial Intelligence, Zhejiang University（浙江大学人工智能研究所）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海高等研究院）；Shanghai AI Laboratory（上海人工智能实验室）；National University of Singapore（新加坡国立大学）
个性化推荐算法通过测量它们之间的距离/相似性来学习用户对项目的偏好。然而，一些现有的推荐模型（例如，矩阵分解）假设用户和项目之间的线性关系。这种方法限制了推荐系统的容量，因为在现实世界的应用程序中，用户和项目之间的交互要比线性关系复杂得多。为了克服这一局限性，本文设计并提出了一个基于符号距离的深度学习框架--基于深度记忆的推荐器（Signed Distance-based Deep Memory Recommender），该框架能够显式和隐式地捕捉用户和物品之间的非线性关系，在一般推荐任务和基于购物篮的推荐任务中都能很好地工作。通过对六个真实数据集的两个推荐任务的广泛实证研究，我们提出的方法取得了显着的改进，超过了十个国家的最先进的推荐模型。
Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models
University of New South Wales（新南威尔士大学）；University of California San Diego（加利福尼亚大学圣迭戈分校）；Indiana University（印第安纳大学）；Adobe Research（奥多比研究院）；CSIRO’s Data61（澳大利亚联邦科学与工业研究组织Data61研究院）
