Cloud Configuration Guidance Tool Using Hybrid Recommender

The system segments the recommendation generation into two distinct modules: collaborative filtering component and content-based filtering component. Each module handles specific aspects of the recommendation task independently, allowing the system to achieve high configuration accuracy while managing complexity through modular architecture. The collaborative filtering module analyzes user behavior patterns, while the content-based module evaluates solution characteristics, and their results are integrated to produce final recommendations.

The system extracts only the most relevant features and data points needed for recommendation generation. Instead of processing all available user data and solution information, the collaborative filtering component extracts key user behavior patterns, while the content-based filtering component extracts critical solution characteristics. This selective extraction reduces data processing volume while maintaining high configuration precision by focusing on the most impactful factors.

The system performs preliminary processing of user data and solution information before real-time recommendation generation. During the configuration process, the collaborative filtering and content-based filtering modules have already established user profiles and solution models in advance. When real-time recommendations are needed, the system only needs to query and integrate these pre-processed data structures, significantly reducing computational load while maintaining fast response times that improve user productivity.