Design of a Individualized Marketing Strategy Integrating User Behavior Analysis and Profile Modeling

In the current fierce market competition, traditional personalized marketing recommendation methods are no longer able to meet the needs of the market. The research mainly proposes a personalized marketing method that combines user behavior analysis and image modeling through the problems of poor personalized data recommendation and insufficient recommendation accuracy for users. The new method solves the problems of spatial deviation of user data through the construction of user image model. At the same time, the new method also analyzes the user behavior marketing data and builds a new personalized marketing method through the bundled recommendation method. The results indicate that the new method achieves the highest accuracy of 0.6531 at a 1% threshold, which is 0.0956 higher than the lowest accuracy obtained by the LR model. The new model achieves the best model performance at neuron 200. The highest recall of the model in the dataset NetEase is 0.4625 and the highest NDCG value is 0.2041. The new model used in the study demonstrates effective personalization for user recommendations. Additionally, it shows superior performance in marketing recommendations compared to existing methods.