Novel SDDM Rating Prediction Models for Recommendation Systems

Abstract

The accuracy of behavioral interactive features is a key factor for improving the performance of rating prediction. In order to deeply explore the potential rules of user behavior and enhance the accurate representation of interactive features, this paper proposes two rating prediction models, based on the spatial dimension and distance measurement (SDDM), under the premise of taking the mean value of the user behavior history as a user feature, and obtaining the interactive features of an item and a user by calculating the distance between them in each feature dimension. In the proposed SDDM-Var and SDDM-PCC models, the variance and the Pearson correlation coefficient (PCC) are respectively utilized to evaluate the user's attention to each feature dimension as to further obtain the weight vector of the interactive features. Finally, in order to improve the generalization ability of the proposed models, the rating prediction is accomplished by means of a specially designed multi-layer full-connection neural network. The conducted experiments with two public MovieLens datasets demonstrate the superior rating prediction performance of the proposed models in comparison with the existing baseline models, in terms of the root mean square error (RMSE), by achieving values of 0.865 and 0.872 on MovieLens 100K, and 0.839 and 0.832 on MovieLens 1M, respectively for SDDM-Var and SDDM-PCC.