scholarly journals Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yimin Cui
Keyword(s):  
Mathematical Modeling ◽  
Data Mining ◽  
Response Time ◽  
User Satisfaction ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
Model Learning ◽  
Before And After ◽  
Purchase Rate ◽  
Click Through Rate

With the advent of the era of big data, data mining has become one of the key technologies in the field of research and business. In order to improve the efficiency of data mining, this paper studies data mining based on the intelligent recommendation system. Firstly, this paper makes mathematical modeling of the intelligent recommendation system based on association rules. After analyzing the requirements of the intelligent recommendation system, Java 2 Platform, Enterprise Edition, technology is used to divide the system architecture into the presentation layer, business logic layer, and data layer. Recommendation module is divided into three substages: data representation, model learning, and recommendation engine. Then, the fuzzy clustering algorithm is used to optimize the system. After the system is built, the performance of the system is evaluated, and the evaluation indexes include accuracy, coverage, and response time. Finally, the system is put into a trial operation of an e-commerce platform. The click-through rate and purchase conversion rate of recommended products before and after the operation are compared, and a questionnaire survey is randomly launched to the platform users to analyze the user satisfaction. The experimental data show that the MAE of this system is the lowest, maintained at about 0.73, and its accuracy is the highest; before the recommended threshold exceeds 0.5, the average coverage rate of this system is the highest: 0.75; in Q1–Q5 subsets, the shortest response time of the system is 0.2 s. Before and after the operation of the system, the average click-through rate increased by 11.04%, and the average purchase rate increased by 9.35%. Among the 1216 users, 43% of the users were satisfied with 4 and 9% with 1. This shows that the system algorithm convergence speed is fast; it can recommend products more in line with user needs and interests and promote higher click-through rate and purchase rate, but user satisfaction can be further improved.

scholarly journals Application of Data Mining to E-Commerce Recommendation Systems

2018 ◽  
Vol 7 (2.32) ◽  
pp. 420
Author(s):  
Dr P.V.R.D. Prasad Rao ◽  
S Varakumari ◽  
Vineetha B ◽  
V Satish
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Information Storage ◽  
Apriori Algorithm ◽  
Rising Power

The rising power of technology has intensely improved the information storage, collection, and manipulation ability. As the information is growing very rapid along with its complexness, data analysis has become more important. The aim of this paper is to recommend products to the user which are more likely to be purchased. This paper, first describes about different techniques for recommendation and the research regarding recommendation system, then suggests a better approach for a good recommendation system and explains the results of that approach. Here, a combination of k-means clustering algorithm and apriori algorithm on transactional dataset so that a better recommendation list can be obtained. 

scholarly journals A Personalized Web Based E-Learning Recommendation System To Enhance and User Learning Experience

2020 ◽  
Vol 9 (1) ◽  
pp. 1186-1195
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
User Behavior ◽  
Search Algorithm ◽  
Learning Experience ◽  
Research Work ◽  
Web Based ◽  
E Learning

The key aim of the data mining techniques is to help the user by reducing the effort for exploring the data, recovering the patterns, and implementing applications that help to find the knowledge specific contents, decision making, and predictions. This research work develops a recommendation system by using the merits of data mining algorithms. They are used for designing web-based e-learning recommendation systems. This model aims to understand the user behavior and contents requirements of the learner. This purpose is solved by obtaining the information from the data source and producing the suggestions of suitable content to the learner. The concept of web content mining and web usage mining has been combined together for performing the required work. This technique involves the genetic algorithm and k-means clustering algorithm for designing the presented model. In this work the k-means clustering algorithm has been used to track user behavior and the genetic algorithm has been used as a search algorithm to find the necessary resources in the database. Finally, the presented system is implemented and its performance is measured. The estimated results demonstrate that the presented model enhances the accuracy of recommendations and also speeds up the computations. A related performance calculation has also provided to justify this conclusion. The obtained results demonstrate that this technique is acceptable for new generation application designs

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

scholarly journals The Sustainable Development of Financial Topic Detection and Trend Prediction by Data Mining

2021 ◽  
Vol 13 (14) ◽  
pp. 7585
Author(s):  
Yunmei Liu ◽  
Shuai Zhang ◽  
Min Chen ◽  
Yenchun Wu ◽  
Zhengxian Chen
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Financial Institutions ◽  
Clustering Algorithm ◽  
Development Trend ◽  
Research Field ◽  
Trend Prediction ◽  
Blockchain Technology ◽  
Supply Chain Finance ◽  
The Government

Blockchain technology is the most cutting-edge technology in the field of financial technology, which has attracted extensive attention from governments, financial institutions and investors of various countries. Blockchain and finance, as an interdisciplinary, cross-technology and cross-field topic, has certain limitations in both theory and application. Based on the bibliometrics data of Web of Science, this paper conducts data mining on 759 papers related to blockchain technology in the financial field by means of co-word analysis, bi-clustering algorithm and strategic coordinate analysis, so as to explore hot topics in this field and predict the future development trend. The experimental results found ten research topics in the field of blockchain combined with finance, including blockchain crowdfunding, Fintech, encryption currency, consensus mechanism, the Internet of Things, digital financial, medical insurance, supply chain finance, intelligent contract and financial innovation. Among them, blockchain crowdfunding, Fintech, encryption currency and supply chain finance are the key research directions in this research field. Finally, this paper also analyzes the opportunities and risks of blockchain development in the financial field and puts forward targeted suggestions for the government and financial institutions.

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