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.
k-nearest-neighbor; Collaborative filtering; Dynamic k; Accuracy.
Birincil Dil | İngilizce |
---|---|
Konular | Mühendislik |
Bölüm | Araştırma Makalesi |
Yazarlar | |
Yayımlanma Tarihi | 31 Mart 2018 |
Yayımlandığı Sayı | Yıl 2018 Cilt: 19 Sayı: 2 |