Semantic representation learning framework for cold-start recommendation in intelligent decision systems.

Abstract

Cold-start recommendation systems encounter a critical constraint that they necessitate historical interaction data, which new user does not possess, resulting in a sparsity bottleneck that diminishes suggestion quality. Traditional approaches require a minimum of N ≥ 10 interactions to generate dependable suggestions, resulting in an average delay of 15 sessions. Recent advancements in hyperbolic geometry and ontological reasoning present interesting avenues nevertheless; they have not been adequately integrated for cold-start situations. This study conceptualizes cold start as semantic manifold alignment, wherein initial user intent is aligned with proxy user regions via ontology reasoning and hyperbolic geometry. HOP-Align derives symbolic preference rationales from section-level attention, encodes them in a 3D Poincare Ball utilizing the exponential map, and executes soft probabilistic alignment to proxy regions. Warm detections transpire when alignment entropy H(p) is less than 0.5 and stabilizes, irrespective of click counts. The system realizes a 76.7% decrease in time-to-warm relative to click based approaches with an HR@10 of 0.55 and conversion rates of 0.13. Significantly, 84.6% of conversion transpires without direct clicks, illustrating the efficacy of attention first design. Entropy based warm detection facilitates customization after 3.5 sessions, as opposed to 15, indicating 77% acceleration in warm-up compared to leading methodologies. HOP-Align illustrates that ontological reasoning and hyperbolic geometry can mitigate essential cold start challenges by processing attention prior to interactions. This method facilitates comprehensible recommendations via concept level reasoning pathway and realizes substantial enhancements in recommendation quality and user experience during the pivotal cold start phase.