scholarly journals Penerapan User-Based Collaborative Filtering Algorithm

2021 ◽  
Vol 20 (2) ◽  
pp. 293-304
Author(s):  
Arfiani Nur Khusna ◽  
Krisvan Patra Delasano ◽  
Dimas Chaerul Ekty Saputra
Keyword(s):  
Root Mean Square Error ◽  
Collaborative Filtering ◽  
Mean Square Error ◽  
Euclidean Distance ◽  
User Acceptance ◽  
Acceptance Test ◽  
Weighted Sum ◽  
Mean Square ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

Sistem online memanfaatkan website sebagai media pemasaran. Namun dengan perkembangan teknologi, pemasaran dilakukan dengan online terdapat kendala yaitu banyaknya produk yang tersedia dalam pemilihan produk. Sistem rekomendasi adalah sistem yang menyarankan informasi berguna atau menduga yang akan dilakukan user untuk mencapai tujuannya, seperti mencari teknik yang terbaik dalam memberikan rekomendasi bagi user. Menurut hasil survey yang telah dilakukan terhadap 17 orang pemakai website pemasaran produk Gadget Shield didapatkan 88,20% mengharapkan adanya penilaian user terhadap produk. Penelitian ini akan melakukan pengembangan sistem rekomendasi produk Gadget Shield pada toko Jackskins menggunakan metode User-Based Collaborative Filtering serta menggunakan Euclidean Distance untuk mengukur jarak kemiripan antar User dan Weighted Sum digunakan untuk mencari rekomendasi produk. Diharapkan dengan adanya sistem dapat memudahkan User dalam pencarian produk Gadget Shield terbaik. Guna menghasilkan produk rekomendasi,hasil nilai kemiripaan dilakukan perhitungan dengan algoritma Weighted Sum. Sistem rekomendasi Collaborative Filtering telah diuji menggunakan metode pengujian akurasi Root Mean Square Error (RMSE) dan pengujian User Acceptance Test (UAT). Hasil uji RMSE menunjukkan nilai 0,496 atau akurasinya 90,08%. Hasil pengujian UAT didapatkan 86,86% diterima. Informasi dari proses tersebutlah yang nantinya diharapkan akan bermanfaat sebagai dasar sumber rekomendasi yang akurat. 

Context-Aware Approach for Restaurant Recommender Systems

2019 ◽  
pp. 324-341 ◽  
Author(s):  
Haoxian Feng ◽  
Thomas Tran
Keyword(s):  
Root Mean Square Error ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Square ◽  
Recommendation Algorithm ◽  
Filtering Method ◽  
User Behaviors ◽  
Svd Method

This chapter addresses the issue of how to effectively use users' historical data in restaurant recommender systems, as opposed to systems, such as FindMe, that only rely on online operations. Towards that end, the authors propose a bias-based SVD method as the underlying recommendation algorithm and test it against the traditional item-based collaborative filtering method on the Entrée restaurant dataset. The results are promising as the obtained root-mean-square-error (RMSE) values reach 0.58 for the SVD and 0.62 for the item-based system. Researchers can extend the transformation from user behaviors to ratings in more application domains other than the restaurant one.

Context-Aware Approach for Restaurant Recommender Systems

2018 ◽  
pp. 1757-1771
Author(s):  
Haoxian Feng ◽  
Thomas Tran
Keyword(s):  
Root Mean Square Error ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Square ◽  
Recommendation Algorithm ◽  
Filtering Method ◽  
User Behaviors ◽  
Svd Method

This paper addresses the issue of how to effectively use users' historical data in restaurant recommender systems, as opposed to systems, such as FindMe, that only rely on online operations. Towards that end, the authors propose a bias-based SVD method as the underlying recommendation algorithm and test it against the traditional item-based collaborative filtering method on the Entrée restaurant dataset. The results are promising as the obtained Root-Mean-Square-Error (RMSE) values reach 0.58 for the SVD and 0.62 for the item-based system. Researchers can extend the transformation from user behaviors to ratings in more application domains other than the restaurant one.

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

scholarly journals Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1020
Author(s):  
Yanqi Dong ◽  
Guangpeng Fan ◽  
Zhiwu Zhou ◽  
Jincheng Liu ◽  
Yongguo Wang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Low Cost ◽  
Reference Value ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Structure ◽  
Mean Square ◽  
Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

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