Research on Personalized Recommendation Model Construction Based on Deep Learning

In order to improve the generalization performance of recommender systems in multi-behavior, multi-scene and crossdomain environments, a personalized recommendation model based on deep modeling is constructed, which integrates a multichannel attention mechanism, a time gating structure and a crossdomain residual migration method to semantically align the user’s multi-behavioral sequences and dynamically model the preferences. A five-layer architecture is designed, covering data preprocessing, feature representation, behavioral modeling, knowledge migration and recommendation prediction modules, and a multi-branch loss function is proposed to jointly optimize recommendation accuracy, interest smoothing and domain consistency. Empirical analysis is conducted on Amazon and MovieLens datasets, and the results show that the constructed model outperforms mainstream baseline models in key metrics such as Precision, Recall, and NDCG, and especially exhibits stronger semantic modeling ability and recommendation robustness in cross-domain recommendation and long sequence prediction tasks.