Information Filtering via Improved Similarity Definition

Abstract

Based on a new definition of user similarity, we introduce an improved collaborative filtering (ICF) algorithm, which could improve the algorithmic accuracy and diversity simultaneously. In the ICF, instead of the standard Pearson coefficient, the user-user similarities are obtained by integrating the heat conduction and mass diffusion processes. The simulation results on a benchmark data set indicate that the corresponding algorithmic accuracy, measured by the ranking score, is improved by 6.7% in the optimal case compared to the standard collaborative filtering (CF) algorithm. More importantly, the diversity of the recommendation lists is also improved by 63.6%. Since the user similarity is crucial for the CF algorithm, this work may shed some light on how to improve the algorithmic performance by giving accurate similarity measurement.