DYNAMIC k NEIGHBOR SELECTION FOR COLLABORATIVE FILTERING

Abstract

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