Effect of rating residual on recommendation quality

The effect of the rating residual on recommendation quality was analyzed. The rating residual was measured through user ratings and latent preferences. Latent preferences were computed with psychometric models. With different levels of rating residual, the effect of the rating residual was experimentally evaluated on real world datasets. Theoretical analysis and experimental results show that rating residual has negative effects on recommendation accuracy and coverage. Based on high quality of data, collaborative filtering algorithms can make precise recommendations for users.