The paper introduces a novel personalized recommendation model called Stacked Noise Reduction Auto Encoder–OCEAN (SDAE-OCEAN), which integrates the OCEAN personality model with a Stacked Denoising Auto Encoder (SDAE) to enhance recommendation accuracy. The OCEAN model, which covers five personality traits (Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism), is used to create a more comprehensive user feature matrix. The SDAE is applied to reduce the dimensionality of user and item feature matrices, extracting deeper information. The integrated user feature matrix is then used with linear regression to predict user ratings for unrated items. The model's effectiveness is evaluated using RMSE and MAE, showing that the SDAE-OCEAN approach improves recommendation accuracy by leveraging the relationship between personality traits and user preferences. The study also explores the impact of hidden layers and iterations on model performance, finding optimal settings for better results. Comparative experiments demonstrate the model's superior performance, particularly in reducing RMSE and MAE values, highlighting the importance of integrating the OCEAN personality model into recommendation systems.The paper introduces a novel personalized recommendation model called Stacked Noise Reduction Auto Encoder–OCEAN (SDAE-OCEAN), which integrates the OCEAN personality model with a Stacked Denoising Auto Encoder (SDAE) to enhance recommendation accuracy. The OCEAN model, which covers five personality traits (Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism), is used to create a more comprehensive user feature matrix. The SDAE is applied to reduce the dimensionality of user and item feature matrices, extracting deeper information. The integrated user feature matrix is then used with linear regression to predict user ratings for unrated items. The model's effectiveness is evaluated using RMSE and MAE, showing that the SDAE-OCEAN approach improves recommendation accuracy by leveraging the relationship between personality traits and user preferences. The study also explores the impact of hidden layers and iterations on model performance, finding optimal settings for better results. Comparative experiments demonstrate the model's superior performance, particularly in reducing RMSE and MAE values, highlighting the importance of integrating the OCEAN personality model into recommendation systems.