A Movie Rating Prediction Method Based on Structured Prior Integration and Target-Aware User Interest with Ridge-Based Calibration

This study examines whether integrating structured priors with target-aware user histories improves movie rating prediction beyond supervised calibration. CDIAC combines a CatBoost prior, a target-aware Deep Interest Network (DIN) fusion branch, and ridge-based calibration (AC). A chronological MovieLens 32M protocol separates model selection, calibration, and testing and uses temporal out-of-fold priors; calibration coefficients remain fixed during inference. Across five runs on 148,738 warm-start test ratings, the model with a 20-epoch training cap achieves RMSE 0.762500 ± 0.000655 and MAE 0.570105 ± 0.000635. Compared with XGBoost + AC using identical calibration labels, the paired RMSE difference is −0.001500, with a user-cluster bootstrap 95% interval of [−0.002834, −0.000195]. Training and inference require approximately 35.09 and 1.49 times as long, respectively. Component ablations support predictive contributions within the tested pipeline, without establishing DIN-specific superiority. AC reduces aggregate RMSE but increases absolute mean signed error in all four rating groups and pooled RMSE for ratings ≤ 2. These findings indicate a modest accuracy gain at substantially higher training cost. The evidence is limited to the evaluated temporal split and warm-start population: 97.87% of test ratings involve users with at least 50 prior interactions. Feature importance and SHAP characterize only the CatBoost branch.