Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has been naturally applied in the development of recommender systems to improve prediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.

推荐系统通过学习用户的先前行为并预测其对特定产品的当前偏好，为用户提供个性化服务支持。人工智能（AI），尤其是计算智能以及机器学习方法和算法，已自然应用于推荐系统的开发中，以提高预测准确性并解决数据稀疏性和冷启动问题。本立场文件系统地讨论了推荐系统中的基本方法和流行技术，以及AI如何有效地改善推荐系统的技术开发和应用。本文不仅回顾了前沿的理论和实践贡献，而且指出了当前的研究问题并指出了新的研究方向。它仔细调查了与使用AI的推荐系统有关的各种问题，并通过使用诸如模糊技术，转移学习，遗传算法，进化算法，神经网络和深度学习以及主动式等AI方法来审查对这些系统的改进学习。本文中的观察将直接支持研究人员和专业人士更好地了解使用AI的推荐系统的当前发展和新方向。