In this study, the impact of the common items between a pair of users on the accuracy of memory-based collaborative filtering (CF) is investigated. Although CF systems are a widely used recommender system, data sparsity remains an issue. As a result, the similarity weight between a pair of users with few ratings is almost a fake relationship. In this work, the similarity weight of the traditional similarity methods is determined using exponential functions with various thresholds. These thresholds are used to specify the size of the common items amongst the users. Exponential functions can devalue the similarity weight between a pair of users who has few common items and increase the similarity weight for users who have sufficient co-rated items. Therefore, the pair of users with sufficient co-rated items obtains a stronger relationship than those with few common items. Thus, the significance of this paper is to succinctly test the impacting of common items on the quality of recommendation that creates an understanding for the researchers by discussing the findings presented in this study. The MovieLens datasets are used as benchmark datasets to measure the effect of the ratio of common items on the accuracy. The result verifies the considerable impact exerted by the factor of common items.

No abstract

The 2019 coronavirus disease (COVID-19) caused pandemic and a huge number of deaths in the world. COVID-19 screening is needed to identify suspected positive COVID-19 or not and it can reduce the spread of COVID-19. The polymerase chain reaction (PCR) test for COVID-19 is a test that analyzes the respiratory specimen. The blood test also can be used to show people who have been infected with SARS-CoV-2. In addition, age parameters also contribute to the susceptibility of COVID-19 transmission. This paper presents the extra trees classification with random over-sampling by considering blood and age parameters for COVID-19 screening. This research proposes enhanced preprocessing data by using KNN Imputer to handle large missing values. The experiments evaluated the existing classification methods such as Random Forest, Extra Trees, Ada Boost, Gradient Boosting, and the proposed Light Gradient Boosting with hyperparameter tuning to measure the predictions of patients infected with SARS-CoV-2. The experiments used Albert Einstein Hospital test data in Brazil that consisted of 5,644 sample data from 559 patients with infected SARS-CoV-2. The experimental results show that the proposed scheme achieves an accuracy of about 98,58%, recall of 98,58%, the precision of 98,61%, F1-Score of 98,61%, and AUC of 0,9682.

Eco-design Collaborative Filtering Recommender System is an approach to assist designers in producing a green product. Collaborative Filtering (CF) approach is the most commonly used and most successful approaches for the systems of recommendation. In eco-design (Ecological Design), several studies focused on the implementation of eco strategies to reduce the products' environmental impact. While the raw materials of the product are even more important in order to design a product to preserve the environment. Therefore, in this paper, the researcher employ the CF to develop a new eco-design method to provide a set of raw materials to assist the designers at early stage to preserve the environment. CF system is able to overcome the information overload issue by analyzing the past behavior of its users. It's very simple and effective way to assist eco-designer to identify the best options from alternatives. CF system introduce a set of recommendations to the product designers through comparing the new product with the existing products in data base based on products' information. Next, determine the most similar products and rank them based on its environmental impact. Then, the components of products which have low environment impact will be provided to the eco-designers as a recommendations. An assumed example of ecodesign will be used to explanation the proposed method. Further research can be conducted on this proposed method by implementing it with real dataset to generalize its performance.