Improved neighborhood-based collaborative filtering

A predominant approach to collaborative filtering is neighborhood based ("k-nearest neighbors"), where a user-item pref- erence rating is interpolated from ratings of similar items and/or users. In this work, we enhance the neighborhood-based approach leading to a substantial improvement of prediction accuracy, with- out a meaningful increase in running time.

First, we remove certain so-called "global effects" from the data to make the different ratings more comparable, thereby improving interpolation accuracy. Sec- ond, we show how to simultaneously derive interpolation weights for all nearest neighbors. Unlike previous approaches where each interpolation weight is computed separately, simultaneous interpo- lation accounts for the many interactions between neighbors by globally solving a suitable optimization problem, also leading to improved accuracy.