Active Recommendation Strategy Based on Multi-Collaborative Agent

With the development of mobile internet platforms, recommendation systems have become crucial in various fields. However, they face several limitations: (1) Traditional recommendation methods focus solely on basic user profile, overlooking other behavioral characteristics of individuals, such as behavioral inertia; (2) Before making a recommendation, they lack a systematic methods to generate emergencies or events that users are concerned about. (3) They neglect collaboration among different agents, including users, scenes and emergencies. In this situation, when emergencies or other events occur, it will be difficult to actively provide behavioral decision recommendations for the current scene. To address these existing challenges, we propose our multi-collaborative agent recommendation model: (1) We introduce a user profiling definition that takes into account behavioral inertia; (2) We propose the CBEOC (Campus-Based-Element-Object-Consequence) model to systematically generate emergencies; (3) We propose an active recommendation strategy based on multi-collaborative agent. We then collect data from multiple universities based on psychological theories to demonstrate the effectiveness of our strategy. Finally, we propose a technical framework for practical application.