Artificial Intelligence for Personalized Marketing in Digital Learning Platforms: Trade-Offs Across Neighborhood, Behaviorally Segmented, and Neural Recommender Models

Abstract

Artificial intelligence (AI) recommender systems operationalize personalized marketing by transforming behavioral data into individualized choice architectures, yet greater model complexity or personalization granularity need not improve every service objective. This study compares population-level item-neighborhood recommendation, behaviorally segmented neighborhood recommendation, and neural collaborative filtering (NCF) across 776,741 deduplicated 1–5 ratings from 646,576 anonymized users and 541 Coursera courses. The primary warm-start evaluation is restricted to 10,296 users (1.59% of the user population) with sufficient interaction history; the remaining sparse-history users contribute to fitting but not to the within-user confirmatory estimand. Configurations are selected only on three validation seeds and then frozen before guarded final evaluation across ten deterministic user-aware seeds. No rank-capable model dominates across objectives: KNN provides the strongest RMSE and personalized-neighborhood coverage profile, ClusteredKNN achieves the strongest observed Top-K retrieval with lower neighborhood support, and a metadata-rich NCF variant achieves the lowest MAE at substantially greater fitting cost. The rating-only UserMean baseline further shows that low numerical prediction error need not imply useful item ranking. Overall, the findings support a contingency view of AI personalization: greater segmentation granularity or model complexity does not inherently create greater value; model choice should instead reflect the service objective, available behavioral evidence, coverage tolerance, operating cadence, and governance requirements.