Case-Intelligence Recommendation on Massive Contents Processing through Dynamic Computing

Abstract

How to suggest a valid recommend within a reasonable time is the greatest technical challenge for the recommendation system, for which tremendous user cases with high dimension are generated while it runs in real time, and these massive data are too difficult to compute directly. This paper proposes a case -intelligence system framework along with a feature -based multi -layer feed -forward neural networks (MFNN) to succeed case-retrieval based on dynamic computing, which constructs the neural networks dependence on the real input vectors instead of the fixed and dull networks structure presupposed, and can apply many kinds of knowledge granularity from various levels effectively to help users for information retrieval and case adaptation. Our subsequent experimental results indicate that it is capable of handling the massive personalized data, and our covering algorithm can decrease the complexity of MFNN algorithm for dynamic computing, which performs adaptable knowledge granularity to enhance the system's efficiency of reasoning.