Application of improved MFNN on dynamic computing for case-intelligence recommendation system

Personalized recommendation involves a process of gathering and storing information about website visitors, from which user's characteristic knowledge is exploited to satisfy the personalized needs. Facing the difficulty of timely identifying new data computing in updating real-time user behaviors, we propose a case-intelligence system framework along with a feature-based multi-layer feed-forward neural networks (MFNN) approach to personalized recommendation that is capable of handling the massive with dynamic data effectively. Our experimental results indicate that better performance in our recommender comes from the both sides: the one is that our MFNN has understandable, constructive and reliable process, unlike the black box of the other ANN networks; the other is our covering algorithm can decrease the complexity of ANN algorithm effectively.