SDP: An Improved Baseline Estimation Model Based On Standard Deviation Proportion
Zhenhua Tan, Danke Wu, Liangliang He, Qiuyun Chang, Bin Zhang · 2019
This paper analyzes the limitation of baseline estimation by defining four kinds of rating personalization corresponding to four kinds of users' rating criterions, including Normal, Strict, Lenient, and Middle. We find a standard deviation proportion pattern from ratings' normal distribution to enhance the handling capability of users' personalized rating behavior, and propose a novel baseline estimation model based on Standard Deviation Proportion, named SDP model, to improve the accuracy of existing recommendation algorithms which used traditional baseline estimation. We also propose two application instances of SDP, including SDPSVD++ and SDPTrustSVD, to show how to apply the proposed SDP. Experiments show that the SDP can not only improve the baseline estimation performance, but also can effectively improve predictive accuracies of existing recommendation algorithms.