Improved neighborhood-based collaborative filtering

Recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item re- lationships. 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. Our method is very fast in practice, generat- ing a prediction in about 0.2 milliseconds. Importantly, it does not require training many parameters or a lengthy preprocessing, mak- ing it very practical for large scale applications. The method was evaluated on the Netflix dataset. We could process the 2.8 million queries of the Qualifying set in 10 minutes yielding a RMSE of 0.9086. Moreover, when an extensive training is allowed, such as SVD-factorization at the preprocessing stage, our method can pro- duce results with a RMSE of 0.8982.