A personalization recommendation method with time characteristics

Abstract

Personalization recommendation can effectively solve the negative influence of information overload to users in the environment of big data. And the existing personalization recommendation model is insufficient in integrating the time characteristics of users' behavior. We build a new extend model of personalization recommendation based on the method of latent factor model. Time characteristics of users' historical behavior are introduced into new model to improve the predictions' accuracy. we implement the new model based on the method of factorization machines, and verify the validity of new model by using the data of movielens dataset. Experimental results demonstrate better performance of new model in improving the predictions' accuracy of users' preferences compared with existing model.