Recommendation Algorithm Using Clustering-Based UPCSim (CB-UPCSim)

被引：6

作者：

Widiyaningtyas, Triyanna ^{[1
,2
]}

Hidayah, Indriana ^{[1
]}

Adji, Teguh Bharata ^{[1
]}

机构：

[1] Univ Gadjah Mada, Dept Elect Engn & Informat Technol, Yogyakarta 55281, Indonesia

[2] Univ Negeri Malang, Dept Elect Engn, Malang 65145, Indonesia

来源：

COMPUTERS | 2021年 / 10卷 / 10期

关键词：

collaborative filtering; memory-based; similarity metrics; k-means clustering; Silhouette Coefficient;

D O I：

10.3390/computers10100123

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

One of the well-known recommendation systems is memory-based collaborative filtering that utilizes similarity metrics. Recently, the similarity metrics have taken into account the user rating and user behavior scores. The user behavior score indicates the user preference in each product type (genre). The added user behavior score to the similarity metric results in more complex computation. To reduce the complex computation, we combined the clustering method and user behavior score-based similarity. The clustering method applies k-means clustering by determination of the number of clusters using the Silhouette Coefficient. Whereas the user behavior score-based similarity utilizes User Profile Correlation-based Similarity (UPCSim). The experimental results with the MovieLens 100k dataset showed a faster computation time of 4.16 s. In addition, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values decreased by 1.88% and 1.46% compared to the baseline algorithm.</p>

引用

页数：17

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