Deep Recommendation Model Architecture and Optimization Strategy for Multimodal User Behavior Fusion

Abstract

With the intensification of the problem of information overload on the Internet, recommendation systems have become the core technology connecting user demands with content supply. Traditional recommendation models rely on single behavioral data or content features, which have drawbacks such as one-sided semantic understanding and insufficient context awareness. Multimodal user behavior fusion technology integrates multi-source heterogeneous information such as text, images, audio, and sensor data to construct a three-dimensional representation of user interests, significantly enhancing the accuracy and personalization level of recommendations. This paper systematically reviews the architectural design principles of multimodal deep recommendation models, analyzes the fusion strategies from three dimensions: the data layer, the feature layer, and the model layer, discusses key technologies such as dynamic weight distribution, cross-modal alignment, and robustness optimization, and proposes optimization paths in combination with engineering challenges such as privacy protection and real-time computing. Research shows that multimodal fusion can increase the click-through rate of recommendation systems by 15% to 25% and the conversion rate by 10% to 18%, providing more intelligent decision support for scenarios such as e-commerce, social media, and content platforms.