Multi-Field-Oriented Personalized Recommendation

Abstract

Considering the relationship among user-to-user, service-to-service, user-to-service, This paper use clustering algorithm to cluster the data, and then re-use model which is based on the population-based recommendation, the content-based recommendation, and collaborative filtering recommendation absorb their benefits and overcome their shortcomings, This paper proposed multi-personalized recommendation service model which is based on the full understanding between interrelated users and services. It can provide users with accurate and personalized service. We verified the model through experiments and carried out the proposed recommendation model drawn feasible.