scholarly journals Sistem Rekomendasi Pembelian Produk Kesehatan pada E-Commerce ABC berbasis Graph Database Amazon Neptune menggunakan Metode Hybrid Content-Collaborative Filtering

2021 ◽  
Vol 12 (2) ◽  
pp. 88
Author(s):  
Made Devayani Dinda Maristha ◽  
Albertus Joko Santoso ◽  
Findra Kartika Sari Dewi
Keyword(s):  
Collaborative Filtering ◽  
Real Time ◽  
Recommendation System ◽  
Graph Database ◽  
Health Product ◽  
Product Categories ◽  
Health Products ◽  
Database Service ◽  
Product Sales ◽  
Business To Business

Abstract. Recommendation System of Health Product Purchasing at ABC E-Commerce System based on Amazon Neptune’s Graph Database using Hybrid ContentCollaborative Filtering Method.Health products purchased by society, either in drugstores or pharmacies may vary according to their needs. ABC e-commerce is a Business to Business (B2B)-based e-commerce owned by PT XYZ. As a health product sales system from distributors to drug stores/pharmacies, they still do not have a health product purchase recommendation system yet. The recommendation system is needed to provide recommendations of health products for the customers. Amazon Neptune is implemented in this research to build a health product recommendation system. The hybrid contentcollaborative filtering method is used to generate complete recommendations based on content attributes and user habits. The datasets were product data, product categories, customers, product principals, and data of products trading. This research produces a health products recommendations model at ABC e-commerce with android based using web services. The implementation can provide recommendations of health products that can be accessed in real-time by customers.Keywords: health products, recommendation systems, graph database, Amazon Neptune, hybrid content-collaborative filteringAbstrak. Produk kesehatan yang dibeli masyarakat, melalui toko obat/apotek, dapat berbeda sesuai kebutuhan. E-commerce ABC berbasis Business to Business (B2B) milik PT XYZ sebagai sistem penjualan produk kesehatan dari distributor kepada toko obat/apotek belum memiliki sistem rekomendasi pembelian produk kesehatan. Sistem rekomendasi sebagai pengembangan fitur e-commerce ABC diperlukan untuk memberikan rekomendasi produk kesehatan yang sesuai dengan keadaan setiap pelanggan. Amazon Neptune sebagai graph database service yang dapat mengelola relasi dalam data yang saling terhubung, digunakan dalam penelitian untuk membangun sistem rekomendasi produk kesehatan. Metode hybrid content-collaborative filtering digunakan untuk menghasilkan rekomendasi yang lengkap berdasarkan atribut konten dan kebiasaan pengguna. Dataset yang digunakan meliputi data produk, kategori produk, pelanggan, principal, serta data jual-beli produk di e-commerce ABC. Penelitian ini menghasilkan model rekomendasi produk kesehatan yang diimplementasikan pada e-commerce ABC berbasis Android menggunakan web service. Implementasi tersebut memberikan rekomendasi produk kesehatan yang dapat diakses secara real-time oleh pelanggan pada saat menggunakan ecommerce ABC.Kata Kunci: produk kesehatan, sistem rekomendasi, graph database, Amazon Neptune, hybrid content-collaborative filtering

scholarly journals Smart Yield Accuracy Prediction using Linear Regression and Collaborative Filtration

2019 ◽  
Vol 8 (3) ◽  
pp. 664-670
Keyword(s):  
Linear Regression ◽  
Soil Fertility ◽  
Collaborative Filtering ◽  
Real Time ◽  
Recommendation System ◽  
Time Data ◽  
Real Time Data ◽  
Crops Yield

India is mainly based on farming. Agriculture is the main source of economy in India, but the farmers are suffering with many problems such as lack of crops yield, lack of water, soil fertility etc. To address those issues this recommendation system is proposed, and it significantly influences the crops yields. The need for the accessible data on the accomplishment for getting crops in good yields are investigated. To accomplish that, real-time data are collected from the farmers from different places of Karnataka. In this paper linear regression and collaborative filtering are used, and results are compared to draw an inference for more accurate recommendation system.

Research on Location-Based Personalized Recommendation System

2014 ◽  
Vol 490-491 ◽  
pp. 1493-1496
Author(s):  
Huan Gao ◽  
Xi Tian ◽  
Xiang Ling Fu
Keyword(s):  
Collaborative Filtering ◽  
Real Time ◽  
Recommendation System ◽  
Information Overload ◽  
Information Filtering ◽  
Mobile Internet ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
New System

With the mobile Internet developing in China, the problem of information overload has been brought to us. The traditional personalized recommendation cannot meet the needs of the mobile Internet. In this paper, the recommendation algorithm is mainly based on the collaborative filtering, but the new factors are introduced into the recommendation system. The new system takes the user's location and friends recommendation into the personalized recommendation system so that the recommendation system can meet the mobile Internet requirements. Besides, this paper also puts forward the concept of moving business circle for information filtering, which realizes the precise and real-time personalized recommendations. This paper also proves the recommendation effects through collecting and analyzing the data, which comes from the website of dianping.com.

scholarly journals The application of e-commerce recommendation system in smart cities based on big data and cloud computing

2021 ◽  
pp. 26-26
Author(s):  
Yiman Zhang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Collaborative Filtering ◽  
Real Time ◽  
Evaluation System ◽  
Recommendation System ◽  
Smart Cities ◽  
Personalized Recommendation ◽  
Current Recommendation ◽  
Recommendation Algorithm

In the era of big data, the amount of Internet data is growing explosively. How to quickly obtain valuable information from massive data has become a challenging task. To effectively solve the problems faced by recommendation technology, such as data sparsity, scalability, and real-time recommendation, a personalized recommendation algorithm for e-commerce based on Hadoop is designed aiming at the problems in collaborative filtering recommendation algorithm. Hadoop cloud computing platform has powerful computing and storage capabilities, which are used to improve the collaborative filtering recommendation algorithm based on project, and establish a comprehensive evaluation system. The effectiveness of the proposed personalized recommendation algorithm is further verified through the analysis and comparison with some traditional collaborative filtering algorithms. The experimental results show that the e-commerce system based on cloud computing technology effectively improves the support of various recommendation algorithms in the system environment; the algorithm has good scalability and recommendation efficiency in the distributed cluster, and the recommendation accuracy is also improved, which can improve the sparsity, scalability and real-time problems in e-commerce personalized recommendation. This study greatly improves the recommendation performance of e-commerce, effectively solves the shortcomings of the current recommendation algorithm, and further promotes the personalized development of e-commerce.

scholarly journals The Design of Web Based Car Recommendation System using Hybrid Recommender Algorithm

2018 ◽  
Vol 7 (3.4) ◽  
pp. 192
Author(s):  
Leyo Babu Thomas ◽  
V Vaidhehi
Keyword(s):  
Collaborative Filtering ◽  
Real Time ◽  
Web Sites ◽  
Recommendation System ◽  
User Model ◽  
Web Based ◽  
Filtering Method ◽  
Recommender Algorithm ◽  
Hybrid Recommender ◽  
Demographic Features

Web based recommendations for any item is mandatory in E-commerce based web sites. This paper is about the design of web based car recommendation system using the hybrid recommender algorithm. The proposed hybrid recommender algorithm is the combination of user-to-user and item-to-item collaborative filtering method to generate the car recommendations. The user model is designed using demographic features, click data and browsing history. Item profile is built using the various attributes of car, 40 brands of car including 224 car types are used in this work. The synthetic dataset of 300 users with 10000 sessions is used to build user model. The proposed algorithm is evaluated with 100 real time users and shows the 83% accuracy in generating recommendations.  

The Application of Cloud Based on Latent Factor Algorithm in Personalized Recommendation

2013 ◽  
Vol 411-414 ◽  
pp. 2288-2291
Author(s):  
Jian Xi Peng ◽  
Zhi Yuan Liu
Keyword(s):  
Big Data ◽  
Collaborative Filtering ◽  
Real Time ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Massive Data ◽  
Latent Factor ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Data Environment

Recommendation system is a commercial marketing method. What more, the system could increase adhesion and satisfaction of consumers to the website which brings great commercial benefit to electronic commerce. But with big data ages coming, it makes a great challenge to real-time recommendation system. As for latent factor class collaborative filtering algorithm, a distributed constructed latent factor algorithm based on cloud is presented in this paper. The algorithm could keep collaborative filtering in good recommendation and ensure the real time in massive data environment. The simulation shows that the algorithm could achieve the recommendation efficiently and quickly. High speedup and scalability are proved.

The Research and Application of MapReduce Based Neighbor Model in Personalized Recommendation

2013 ◽  
Vol 373-375 ◽  
pp. 1674-1677
Author(s):  
Jian Xi Peng ◽  
Zhi Yuan Liu
Keyword(s):  
Collaborative Filtering ◽  
Real Time ◽  
Recommendation System ◽  
High Efficiency ◽  
Personalized Recommendation ◽  
Massive Data ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Computing Framework ◽  
Model Algorithm

Personalized recommendation provides convenience to users and brings more benefit to companies as well. It has been an important part of electronic commerce website. Collaborative filtering is a common algorithm in recommendation system. But with massive data ages coming, traditional collaborative filtering algorithm could not finish recommendation in time. A neighbor model algorithm based on MapReduce distributed computing framework is presented against to collaborative filtering algorithm. The presented algorithm could accomplish the personalized recommendation effectively and meet the real time requirement completely. The simulation shows that the algorithm has high efficiency and could complete the recommended in a highly efficient and real-time.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

scholarly journals Extrapolative Collaborative Filtering Recommendation System with Word2Vec for Purchased Product for SMEs

2021 ◽  
Vol 13 (13) ◽  
pp. 7156
Author(s):  
Kyoung Jun Lee ◽  
Yu Jeong Hwangbo ◽  
Baek Jeong ◽  
Ji Woong Yoo ◽  
Kyung Yang Park
Keyword(s):  
Collaborative Filtering ◽  
High Performance ◽  
Recommendation System ◽  
Small And Medium Enterprises ◽  
Personal Information ◽  
Business Environment ◽  
Share Data ◽  
Artificial Intelligence Technology ◽  
Medium Enterprises ◽  
Start Ups

Many small and medium enterprises (SMEs) want to introduce recommendation services to boost sales, but they need to have sufficient amounts of data to introduce these recommendation services. This study proposes an extrapolative collaborative filtering (ECF) system that does not directly share data among SMEs but improves recommendation performance for small and medium-sized companies that lack data through the extrapolation of data, which can provide a magical experience to users. Previously, recommendations were made utilizing only data generated by the merchant itself, so it was impossible to recommend goods to new users. However, our ECF system provides appropriate recommendations to new users as well as existing users based on privacy-preserved payment transaction data. To accomplish this, PP2Vec using Word2Vec was developed by utilizing purchase information only, excluding personal information from payment company data. We then compared the performances of single-merchant models and multi-merchant models. For the merchants with more data than SMEs, the performance of the single-merchant model was higher, while for the SME merchants with fewer data, the multi-merchant model’s performance was higher. The ECF System proposed in this study is more suitable for the real-world business environment because it does not directly share data among companies. Our study shows that AI (artificial intelligence) technology can contribute to the sustainability and viability of economic systems by providing high-performance recommendation capability, especially for small and medium-sized enterprises and start-ups.

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

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