Within the contemporary digital landscape characterized by unprecedented informational deluge, short-form video platforms have profoundly reconfigured global users paradigms for entertainment consumption and knowledge acquisition. This transformative impact stems from the mediums inherent attributes: temporal immediacy, content fragmentation, and profoundly immersive engagement. Nevertheless, amidst exponential expansion of user demographics and unprecedented diversification of content ecosystems, conventional recommendation algorithms persistently encounter substantive impediments. These manifest in deficiently capturing ephemeral content relevance, implementing granular operational differentiation across heterogeneous user segments, and unearthing latent correlations within complex behavioral sequences. Leveraging a comprehensive empirical dataset (encompassing 122,500 discrete behavioral traces) sourced from a preeminent short-video platform, this investigation employs multifaceted data excavation and sophisticated model architecture to elucidate profound interconnections between user behavioral archetypes and recommendation optimization strategies. The seminal contribution of this research transcends the traditional systems constrained content-centric paradigm. It pioneeringly amalgamates temporal dynamics, nuanced behavioral differentiation across cohorts, and adaptive feedback mechanisms within a cohesive analytical framework. Consequently, it furnishes both conceptual underpinnings and actionable methodologies for developing intelligent recommendation engines capable of delivering hyper-personalized experiences-effectively actualizing the vision of contextually attuned personalization for diverse users across manifold temporal instances.
Sun Zaozhuang (Tue,) studied this question.