Dynamic k Neighbor Selection for Collaborative Filtering

Yıl 2018, Cilt: 19 Sayı: 2, 303 - 315, 31.03.2018

Halil Zeybek Cihan Kaleli

https://doi.org/10.18038/aubtda.346407

Cited By: 3

Öz

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.

Anahtar Kelimeler

k-nearest-neighbor; Collaborative filtering; Dynamic k; Accuracy.

Kaynakça

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