Adapting Ratings in Memory-Based Collaborative Filtering using Linear Regression

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 wellknown 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.

• Type: Conference
• Year: 2007
• Where: IRI
• Authors: Jérôme Kunegis, Sahin Albayrak
• Keywords: Information Retrieval, IRI 2007, Memory-based Prediction Algorithm, Prediction Algorithm, Rating Prediction Algorithm