Application research of personalized recommendation method based on Grey theory
Jin Dai, Yannan Sun, Mei Wang, Huijie Liu · 2017
There are two major challenges to the personalized recommendation method, one is the sparseness of characteristic attribute, the other is the excessive reliance on scoring data. To solve above problems, a personalized recommendation algorithm (PRM-Grey) based on grey theory is presented. Firstly, the nearest neighbor matrix formed through the similarity between the characteristic matrix rows. Then, PRM-Grey combines with the corresponding data scores make recommendations. It can effectively solve characteristic attribute spares of personalized recommendation. On this basis, PRM-Grey imports grey relational analysis to measure the similarity of characteristic matrix, and uses grey prediction model to make personalized recommendation. Experiments show: compared to traditional personalized recommendation method, the accuracy of the PRM-Grey gains an average 10%. It fully illustrates the effectiveness of PRM-Grey.