Dynamic k Neighbor Selection for Collaborative Filtering

Abstract

Collaborative filtering is a commonly used method to reduce information overload. It is widely used in recommendation systems due to its simplicity. In traditional collaborative filtering, recommendations are produced based on similarities among users/items. In this approach, the most correlated k neighbors are determined, and a prediction is computed for each user/item by utilizing this neighborhood. During recommendation process, a predefined k value as a number of neighbors is used for prediction processes. In this paper, we analyze the effect of selecting different k values for each user or item. For this purpose, we generate a model that determines k values for each user or item at the off-line time. Empirical outcomes show that using the dynamic k values during the k-nn algorithm leads to more favorable recommendations compared to a constant k value.