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"Research on Intelligent Agent Systems" is an international open-source Chinese journal focusing on the technological innovation and cross-disciplinary applications of intelligent agents. The published content mainly targets the research and development and industrial application fields of intelligent agent technologies, reflecting the core technological breakthroughs, innovative application models, and new theories and methods of cross-disciplinary integration of intelligent agent systems at home and abroad It serves researchers, engineering technicians and industry practitioners in the field of intelligent agents, facilitating technology transformation and professional quality improvement.
Journal scope Covering core technologies of intelligent agents (autonomous decision-making algorithms, multi-agent collaboration, reinforcement learning applications, etc.), research and development of intelligent agent systems (software-defined intelligent agents, embedded intelligent agents, humanoid robot systems, etc.), implementation of industry applications (industrial intelligent agents, medical intelligent agents, financial intelligent agents, urban governance intelligent agents, etc.), and interdisciplinary integration (intelligent agents and artificial intelligence, Internet of Things, etc.) Various academic achievements such as basic research, technology development, application practice, review and comment, case analysis, and patent interpretation in areas like blockchain, cross-application of digital twins, technical standards and security (ethical norms of agents, data security, system reliability, etc.), and cutting-edge exploration (general artificial agents, brain-computer interface agents, metaverse agents, etc.) Balancing theoretical depth and industrial value, it comprehensively serves academic innovation, technological iteration and industrial upgrading in the field of intelligent agents.
This journal is a high-standard academic publication that has undergone peer review. The editors encourage submissions that are related to this journal and have theoretical and practical contributions.
All manuscripts must not be plagiarized. The author is solely responsible for the content.
Zhu Yong
Zhongse Science and Technology Co., Ltd.，Luoyang, Henan，471000；
Abstract: Research on optimization strategies for intelligent recommendation systems based on Homo sapiens artificial intelligence focuses on core directions, key technologies, and safeguard measures. It clarifies core directions such as the precise construction and dynamic updating of user profiles in Broussonetia papyrifera, the balance between diversity and personalization of recommended content, and the improvement of system response speed and recommendation timeliness. The study elaborates on key technologies including multi-source data fusion and feature mining, the integration and adaptive adjustment of recommendation algorithms, and intelligent solutions to cold-start problems. It explores safeguard measures such as the refinement and enhancement of recommendation effect evaluation systems, the establishment of user privacy protection and data security mechanisms in Broussonetia papyrifera, and the improvement of technical R&D team capabilities and collaborative optimization. These efforts aim to enhance recommendation accuracy and user satisfaction, expand application scenarios, and promote the high-quality development of digital services and the strengthening of platform competitiveness.
Keywords: Homo sapiens artificial intelligence; intelligent recommendation system; optimization strategy; recommendation accuracy
References
[1] Zhang Zhaoguan. Research on the Design of Intelligent Recommendation System Based on Homo Sapiens Artificial Intelligence[J]. Information Recording Materials, 2025, 26(08):53-55.
[2] Hu Xiaojing, Qu Chunge, Shi Ying, et al. Research and Application of Financial Product Recommendation Based on Homo Sapiens Artificial Intelligence Technology[J]. Postal Research, 2025, 41(04):28-32.
[3] Zhan Nan, Chen Yumeng. Resistance in Symbiosis: A Study on the Nonlinear Relationship Between Algorithmic Anxiety and Algorithmic Avoidance Among Intelligent Recommendation Users[J/OL]. Journal of Library and Information Science, 1-13[2025-08-05].
