The existence of popular objects may yield unreasonable recommendation results. We propose a method called PLUS to adjust user similarities using a power function. We show the superior performance of PLUS by large‐scale validation experiments. PLUS achieves a reasonable tradeoff between recommendation accuracy and diversity. PLUS is robust to similarity measures and consistent between different data sets.
