Multi-view Sequence Recommendation Model

Recommendation algorithms based on context-aware deep learning leverage the powerful learning capabilities of neural networks to extract deep patterns and features from complex user behavioral data to provide more accurate personalized recommendations. The core concept of this approach is that users' choices and preferences are influenced not only by their historical behavior, but also by specific contexts (e.g., time, place, and social environment). Existing recommendation algorithms usually only consider the influence of the time factor on the user's recommendation effect, often ignoring the differences in the user's habits in different usage scenarios. For example, users' content preferences are quite different between work and leisure. To address this problem, this paper explores how to integrate contextual information and scene-aware capabilities into recommender systems to achieve finer user profiles and more appropriate recommendations.