Proactive Hybrid Recommendation Advertising: A Novel Advertising Format for Generative AI Based on AI-Initiated Lists of Organic and Sponsored Recommendations

The emergence of generative artificial intelligence (AI) is creating new possibilities for integrating commercial communication into conversational interfaces. Conventional digital advertising typically relies on user-initiated searches, explicitly requested recommendations, advertisements embedded in content, or sponsored placements within established recommendation feeds. Generative AI introduces the possibility of a different advertising interaction in which the system independently identifies an appropriate opportunity during an ongoing conversation and proactively initiates a complete recommendation experience. This paper proposes Proactive Hybrid Recommendation Advertising (PHRA), a conceptual advertising framework in which a generative AI system proactively presents a unified recommendation list containing both organic recommendations and paid sponsored recommendations without waiting for the user to explicitly request product or service recommendations. The central contribution is the conceptualization of the entire AI-initiated hybrid recommendation list as a distinct advertising unit, rather than treating advertising as an isolated sponsored item appended to an otherwise organic recommendation response. Within this framework, organic recommendations are selected independently according to their relevance and potential value to the user, whereas sponsored recommendations are included through identifiable commercial arrangements with advertisers. Both categories appear within one coordinated recommendation presentation but remain transparently distinguishable in terms of commercial status, selection rationale, and sponsorship. The proposed architecture introduces conversational-context analysis, proactive opportunity identification, hybrid-list construction, organic and sponsored recommendation allocation, commercial disclosure, presentation management, and user-control mechanisms. It further establishes safeguards intended to prevent paid placement from silently determining organic recommendation quality or being misrepresented as an independent AI judgment. The framework distinguishes itself from conventional sponsored recommendation systems by combining AI-initiated presentation, unsolicited recommendation-list generation, and the integration of organic and sponsored recommendations into one unified advertising format. This paper develops the conceptual foundations, architectural components, operational workflow, design requirements, evaluation propositions, and ethical considerations of PHRA. It does not claim experimentally demonstrated effectiveness or verified commercial advantages. To the best of the author's knowledge, the specific combination of a proactively initiated, unified organic–sponsored recommendation list, conceptualized as an independent advertising format for generative AI conversations, represents a distinct architectural proposal that warrants further systematic investigation.