scholarly journals Game User Preference Data Analysis and Market Guidance Based on Dynamic Attention GRU

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xuelian Yang ◽  
Jin Bai ◽  
Xiaolin Wang
Keyword(s):  
Data Mining ◽  
User Behavior ◽  
Labor Cost ◽  
Internet Technology ◽  
Social Model ◽  
User Preference ◽  
Data Sets ◽  
Marketing Model ◽  
Dynamic Attention ◽  
The Cost

With the development of Internet technology and social model, game products have become an important product of people’s life for entertainment and recreation, and the precise marketing of game products has become a winning means for enterprises to improve competitiveness and reduce labor cost consumption, and major game companies are also paying more and more attention to the data-based marketing model. How to dig out the effective information from the existing market behavior data is a powerful means to implement precise marketing. Achieving precise positioning and marketing of gaming market is the guarantee of innovative development of game companies. For the research on the above problem, based on the SEMAS process of data mining, this paper proposes a mining model based on recurrent neural network, which is named as Dynamic Attention GRU (DAGRU) with multiple dynamic attention mechanisms, and evaluates it on two self-built data sets of user behavior samples. The results demonstrate that the mining method can effectively analyze and predict the player behavior goals. The game marketing system based on data mining can indeed provide more accurate and automated marketing services, which greatly reduces the cost investment under the traditional marketing model and achieves accurate targeting marketing services and has certain application value.

Knowledge Discovery from Online Communities

Data Mining ◽  
2013 ◽  
pp. 1230-1252
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
User Behavior ◽  
Network Models ◽  
Social Model ◽  
User Generated Content ◽  
Growth Trend ◽  
User Behaviors ◽  
Application Systems

This chapter presents an overview of social network features such as user behavior, social models, and user-generated content to highlight the most notable research trends and application systems built over such appealing models and online media data. It first describes the most popular social networks by analyzing the growth trend, the user behaviors, the evolution of social groups and models, and the most relevant types of data continuously generated and updated by the users. Next, the most recent and valuable applications of data mining techniques to social network models and user-generated content are presented. Discussed works address both social model extractions tailored to semantic knowledge inference and automatic understanding of the user-generated content. Finally, prospects of data mining research on social networks are provided as well.

Data Mining for Fraud Detection System

2011 ◽  
pp. 411-416
Author(s):  
Roberto Marmo
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
User Behavior ◽  
Detection System ◽  
Fraud Detection ◽  
Modern Technology ◽  
Data Sets ◽  
Security Measures ◽  
Economic Activities ◽  
Huge Data

As a conseguence of expansion of modern technology, the number and scenario of fraud are increasing dramatically. Therefore, the reputation blemish and losses caused are primary motivations for technologies and methodologies for fraud detection that have been applied successfully in some economic activities. The detection involves monitoring the behavior of users based on huge data sets such as the logged data and user behavior. The aim of this contribution is to show some data mining techniques for fraud detection and prevention with applications in credit card and telecommunications, within a business of mining the data to achieve higher cost savings, and also in the interests of determining potential legal evidence. The problem is very difficult because fraudsters takes many different forms and are adaptive, so they will usually look for ways to avoid every security measures.

scholarly journals Analisis Algoritma K-Means Clustering Untuk Menentukan Strategi Promosi Penjualan Sepeda Motor Studi Kasus PT. Alfa Scorpii

JUTI UNISI ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Abdul Muni
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Sales Promotion ◽  
Large Data Sets ◽  
Data Sets ◽  
Promotion Strategy ◽  
Corporate Earnings ◽  
Clustering Data ◽  
The Right ◽  
The Cost

PT. Alpa Scorpii is the sector private the economy in the motorcycle sales. The utilization of the data is not maximum, sales report that is used only limited to report. Promotion strategy is to increase the income of the company in relation to the straight way with the cost. The data mining so that data can be used as the existing knowledge from the large data sets or with the term knowledge discovery or pattern recognition. Many methods in data mining one only with the method the algorithm K-Means the Cluster. Clustering  data so that the field of marketing can perform the motor sales promotion strategy to new customers with the right and can improve corporate earnings.

Data Mining to Examine the Treatment of Osteomyelitis

2010 ◽  
pp. 15-46
Author(s):  
Hamid Zahedi
Keyword(s):  
Data Mining ◽  
Relevant Information ◽  
Background Information ◽  
Primary Data ◽  
Data Sets ◽  
National Inpatient Sample ◽  
Mining Methods ◽  
Access To Data ◽  
The Cost ◽  
Online Sources

The purpose of this study is to use data mining methods to investigate the physician decisions specifically in the treatment of osteomyelitis. Two primary data sets have been used in this study; the National Inpatient Sample (NIS) and the Thomson MedStat MarketScan data. We used online sources to obtain background information about the disease and its treatment in the literature. An innovative method was used to capture the information from the web and to cluster or filter that information to find the most relevant information, using SAS Text Miner. Other important innovations in investigating the data include finding the switches of medication and comparing the date of the switch with the date of procedures. We could study these medications switched at deeper levels, but this is not necessary in our study, especially with limited access to data. We also create a model to forecast the cost of hospitalization for patients with osteomyelitis.

Research on Structure and Model of E-Commerce Based on Web Mining

2010 ◽  
Vol 143-144 ◽  
pp. 338-342
Author(s):  
Xu Hao
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
High Speed ◽  
Web Mining ◽  
Data Sets ◽  
Individual Data ◽  
Specific Domain ◽  
Transaction Data ◽  
The Cost ◽  
Very High

As one of the key applied forms on Internet, E-commerce has been growing at a very high speed in China in recent years. Starting from the perspective that E-commerce consumers pursue low price while retailers try to decrease the cost. Data mining is a form of knowledge discovery essential for solving problems in a specific domain. Individual data sets may be gathered and studied collectively for purposes other than those for which they were originally created. Data mining is most commonly used in attempts to induce association rules from transaction data.

scholarly journals Data Mining Approach in Retail Knowledge Discovery and Internet Technologies

2018 ◽  
Vol 7 (2) ◽  
pp. 100-105
Author(s):  
Simranjit Kaur ◽  
Seema Baghla
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Data ◽  
Internet Technology ◽  
Categorical Variables ◽  
Data Sets ◽  
Rule Mining ◽  
Data Set ◽  
Demographic Profiles

Online shopping has a shopping channel or purchasing various items through online medium. Data mining is defined as a process used to extract usable data from a larger set of any raw data. The data set extraction from the demographic profiles and Questionnaire to investigate the gathered based by association. The method for shopping was totally changed with the happening to internet Technology. Association rule mining is one of the important problems of data mining has been used here. The goal of the association rule mining is to detect relationships or associations between specific values of categorical variables in large data sets.

Knowledge Discovery from Online Communities

2011 ◽  
pp. 123-145
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
User Behavior ◽  
Network Models ◽  
Social Model ◽  
User Generated Content ◽  
Growth Trend ◽  
User Behaviors ◽  
Application Systems

This chapter presents an overview of social network features such as user behavior, social models, and user-generated content to highlight the most notable research trends and application systems built over such appealing models and online media data. It first describes the most popular social networks by analyzing the growth trend, the user behaviors, the evolution of social groups and models, and the most relevant types of data continuously generated and updated by the users. Next, the most recent and valuable applications of data mining techniques to social network models and user-generated content are presented. Discussed works address both social model extractions tailored to semantic knowledge inference and automatic understanding of the user-generated content. Finally, prospects of data mining research on social networks are provided as well.

scholarly journals Matching Users’ Preference under Target Revenue Constraints in Data Recommendation Systems

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 205 ◽  
Author(s):  
Shanyun Liu ◽  
Yunquan Dong ◽  
Pingyi Fan ◽  
Rui She ◽  
Shuo Wan
Keyword(s):  
User Behavior ◽  
User Preference ◽  
Importance Measure ◽  
Data Sets ◽  
Target Revenue ◽  
Importance Coefficient ◽  
Expected Revenue ◽  
Utility Distribution ◽  
The One ◽  
Recommendation Strategy

This paper focuses on the problem of finding a particular data recommendation strategy based on the user preference and a system expected revenue. To this end, we formulate this problem as an optimization by designing the recommendation mechanism as close to the user behavior as possible with a certain revenue constraint. In fact, the optimal recommendation distribution is the one that is the closest to the utility distribution in the sense of relative entropy and satisfies expected revenue. We show that the optimal recommendation distribution follows the same form as the message importance measure (MIM) if the target revenue is reasonable, i.e., neither too small nor too large. Therefore, the optimal recommendation distribution can be regarded as the normalized MIM, where the parameter, called importance coefficient, presents the concern of the system and switches the attention of the system over data sets with different occurring probability. By adjusting the importance coefficient, our MIM based framework of data recommendation can then be applied to systems with various system requirements and data distributions. Therefore, the obtained results illustrate the physical meaning of MIM from the data recommendation perspective and validate the rationality of MIM in one aspect.

scholarly journals Personalized Recommendation for Mobile Internet Wealth Management Based on User Behavior Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiangyu Ye ◽  
Mengmeng Chen
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
User Behavior ◽  
Mobile Internet ◽  
Internet Technology ◽  
Personalized Recommendation ◽  
Transaction Data ◽  
Wealth Management ◽  
Other Information ◽  
Management Issues

Economic development has provided good opportunities for the development of securities companies. Similarly, the development of Internet technology has also brought huge opportunities and challenges to the development of securities companies. Aiming at the current wealth management issues in the era of mobile Internet, this article attempts to develop a personalized recommendation approach on the basis of users’ behavioral data analysis. We analyzed and judged the current situation of mobile Internet wealth management using personalized recommendation systems. On the basis of personalized recommendation, we use the user’s interest tags, personalized recommendation technology, and data mining technology to analyze and summarize customer transaction records. This is done through the use of preservation of customer transaction data. By understanding customers’ investment needs, risk preferences, and other information, we can segment customers and provide them with targeted products and services. As a result of the study, a flexible personalized recommendation framework is designed and validated for mobile Internet wealth management services. The effectiveness of the proposed approach is verified through testing of the developed model.

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