Quantum-inspired recommendation approach based on user holographic perception under deep collaboration of large model

Recommender systems powered by highly large model-collaboration can rapidly align with users’ preconceived expectations. However, conventional recommendations fail to fully consider the coupling of user behavior factors and user’s real motives hidden under high dependency of large models. In turn, the superficiality of user portraits causes dimensional collapses of recommendation tasks, giving rise to phenomena such as consumption trajectory constriction and preference rigidity. To address the above issues, this paper proposes a Quantum-Inspired Recommendation Approach Based on User Holographic Perception under Deep Collaboration of Large Model (QIHP), enabling recommendation extrapolation driven by users’ inherent motivations. First, a quantum spatial representation model in large model micro-environments is established. By proposing a progressive dissociation strategy of psychology/character capsules, user “nucleus” sustainable basic portraits are constructed. Then a quantum subnet collaborative method is proposed, emphasizing the extraction of implicit entanglement patterns in users’ behaviors. User shopping internal drives are stripped away to facilitate the construction of user “sand” refined decision-making portraits. Finally, a quantum state shunt attention is introduced to model user latent behavior patterns of trend-burst-mimicry. By harnessing the quantum tunneling mechanism, excessively entangled quantum correlations are disentangled, enabling the reconstruction of emergent hypergraphs that to establish user “cloud” self-organized sensory portraits. Building upon the “nucleus-sand-cloud” holographic polymorphic portraits of users, we develop a multi-task inference extrapolation theory. This leverages quantum fuzzy logic interventions to exploit multi-task inference extrapolation theory that satisfy users’ non-preconceived expectations. Experimental results show that QIHP has substantial enhancements, providing a new solution for recommendations in large model contexts.