Abstract

We show that the standard memory-based collaborative filtering rating prediction algorithm using the Pearson correlation can be improved by adapting user ratings using linear regression. We compare several variants of the memory-based prediction algorithm with and without adapting the ratings. We show that in two well-known publicly available rating datasets, the mean absolute error and the root mean squared error are reduced by as much as 20% in all variants of the algorithm tested.

@INPROCEEDINGS{kunegis07b,
  author = {Jerome Kunegis and Sahin Albayrak},
  title = {Adapting Ratings in Memory-Based Collaborative Filtering using Linear
	Regression},
  booktitle = {Proceedings of the 2007 IEEE International Conference on Information
	Reuse and Integration},
  year = {2007},
  publisher = {IEEE Computer Society Press},
  owner = {mehlitz},
  timestamp = {2007.09.05}
  pages={49--54}
}