Electronic Commerce Recommendation Mechanism Based on QoS and Bayesian Model

Abstract

Electronic commerce recommender systems represent personalized services that want to predict users interest on information items. However, traditional recommendation system has suffered from its shortage in scalability as their calculation complexity increases quickly both in time and space when the number of the user and item in the rating database increases. Poor quality is also one challenge in electronic commerce recommender systems. The paper proposed an electronic commerce recommendation mechanism based on QoS and Bayesian model. And the proposed recommender method combining QoS and Bayesian can improve the accuracy of the electronic commerce recommendation system.