Beyond Ratings: Integrating Spatial Data and Behavioral Cues in Modern Prediction Systems

Abstract

Modern prediction systems, especially those driving recommender engines and intelligent decision tools, have long relied on ratings as the primary indicator of user preference. However, this approach fails to capture the complexity of real-world behavior, particularly in dynamic environments where context, location, and user interactions offer richer predictive signals. This study introduces a hybrid predictive framework that integrates spatial data and behavioral cues alongside traditional rating systems. Drawing on a multilayered dataset collected from a real-world application ecosystem, including user ratings, geolocation logs, and clickstream behavior, we developed an ensemble deep learning model combining convolutional neural networks (CNNs), attention mechanisms, and gradient-boosted decision trees (GBDT). The model was evaluated using AUC, F1-score, and precision-recall metrics against standard collaborative filtering baselines. Results show a significant improvement in predictive accuracy, with a 14.7% increase in F1-score and 11.2% gain in AUC over baseline models. The inclusion of spatial embeddings and temporal-behavioral sequences contributed to better personalization, reduced prediction sparsity, and improved responsiveness to context shifts. The findings suggest that spatial-behavioral integration not only enhances accuracy but also provides deeper insights into user intent and preference volatility. This paper demonstrates the feasibility and effectiveness of expanding beyond rating-based paradigms and offers a blueprint for next-generation prediction systems in sectors such as healthcare, smart retail, and personalized services.