Research on Recommender System Based on Curiosity Guided Identity Modification

Abstract

Faced with the problem of information overload of big data, multi-factor fusion is the key technology of recommendation systems. How to provide personalized products for users accurately is the demand of recommendation system. Therefore, a new nearest neighbor algorithm is proposed to fuse the two kinds of identity and use curiosity as guidance to mining hidden information more efficiently, although the algorithm of curiosity modified identification degree swings in a small range, other evaluation indexes are improved. The improvement of the Receiver Operating Characteristic (ROC) curve shows that the robustness and improvement degree of the sub-algorithm is more significant.