User's Basic Information Research Articles

Research on Consumer Behavior Prediction Based on E-commerce Data Analysis

The user behavior log of e-commerce platform contains a wide range of data, which can be roughly divided into three types: user basic information, merchant information and user behavior information. Behavioral information includes clicks, favorites, shopping carts and purchases. Because of the asymmetry of online shopping information, the virtuality of trading environment, the incomplete perception of goods and the unsynchronization of trading process, consumers are faced with many uncertainties in the process of online shopping. According to the specific research problems, this paper applies the real consumer behavior data in the e-commerce data platform to build a model to predict consumer behavior. The consumer behavior sequence and other characteristics are analyzed by two machine learning methods respectively. First, the consumer behavior sequence based on time sequence is analyzed. Add the obtained probability value as a new feature to other feature sets as the input of NB (Naive Bayes) model, and train the model to get the probability of whether consumers will buy, and then get the final judgment result of the model. The results show that the fusion model has a balanced performance for consumer behavior sequences with different lengths, and its accuracy can be kept at around 0.9. The research on the problems raised in this paper can also be extended to other fields.

Assessment Method for Demand Side User Response Potential based on Game Theory and Information Value

An accurate assessment of user demand response potential is helpful for power companies or load aggregators to rationally call demand-side resources, improve the implementation effect of demand response, and reduce the peak-valley load difference of power systems. This paper proposes a new approach based on game theory and information value to evaluate the response potential of demand-side users. Firstly, the evaluation index system of user response potential is established based on user history behaviors, user basic information, aggregator’s incentive to users, and user’s information value. Secondly, based on game theory, the total of subjective and objective weights is integrated, and the game solution of evaluation index weight is obtained. Finally, the user potential assessment results are quantified based on the fuzzy optimization set pair analysis theory to achieve an accurate online assessment of the demand-side user response potential.

An Rfid-Based Access Control System Using Electromagnetic Door Lock and an Intruder Alert System

Aims: The study was conducted to develop a basic security system that integrates Radio Frequency Identification (RFID), an electromagnetic lock, and a GSM Module to produce a lowcost and effective access control system. This work uses the possibility of improving access control door security by replacing a door key with a dependable electromagnetic door lock system for only authorized persons utilizing RFID and a mobile call intruder warning system.
 Methodology: The system is controlled by an ATMEGA328 microcontroller, which instructs the RFID reader to check and verify the users' unique identity tags before unlocking the electromagnetic lock to grant access or alert the administrators through the GSM module if an invalid tag is used. The microcontroller which also controls a liquid crystal display was programmed using Arduino C language; the data of identification tags were stored in its database. The RFIDbased access control with electromagnetic lock and intruder alert consists of three main parts – the INPUT (RFID), CONTROL (ATMega 328), and OUTPUT (Electromagnetic lock, LCD, Buzzer, and GSM Module). The RFID reader was able to scan tags and open a prototype door when a valid tag is used. The motor closes the door after a preset 5 seconds delay.
 Conclusion: When compared to well-known techniques in the literature, the developed RFID Access control system outperforms them in terms of accuracy and cost-effectiveness. The door locking system works in real-time because when the user places the tag in contact with the reader, the door opens and the check-in information, together with the user's basic information, is recorded in the central server.

User online purchase behavior prediction based on fusion model of CatBoost and Logit

Analyzing users’ consumption preferences and willingness based on massive online behavior data can help optimize user experience and improve product purchase rate. For the online platform user data, feature engineering methods were used to pre-process, visualize, analyze and construct features. Then, a prediction and evaluation model of users’ online purchase behavior was developed based on the CatBoost algorithm, and the important features affecting users’ purchase behavior were quantitatively analyzed using the Logit Model. Compared with Random Forest, Support Vector Machine and XGBoost, the prediction model has better performance with prediction accuracy of 98.38% and F1 score of 0.775. The results show that user basic information, user access data and user login data have impact on user online purchase behavior. Especially the number of visits with or without coupons, unfinished orders of annual classes, number of coupons received, repeated learning of courses, public number following, days of login, city and number of learning classes sections are important features affecting purchase behavior.

Integrating User Reviews and Ratings for Enhanced Personalized Searching

With the development of e-commerce, websites such as Amazon and eBay have become very popular. Users post reviews of products and rate the helpfulness of reviews on these websites. Reviews written by a user and reviews rated by a user reflect the user's interests and disinterest. Thus, they are very useful for user profiling. In this study, the authors explore users' reviews and ratings of reviews for personalized searching and propose a review-based user profiling method. To satisfy a user's basic information needs, expressed in the form of a query, they also propose a priority-based result ranking strategy. For evaluation, they conduct experiments on a real-life data set. The experimental results show that their method can significantly improve retrieval quality.

IMPLEMENTING FUZZY LOGIC TO GENERATE USER PROFILE IN DECISIONAL DNA TELEVISION: THE CONCEPT AND INITIAL CASE STUDY

In this article, we present a concept and case study of a novel approach that generates a television (TV) user's profile utilizing principles of fuzzy logic. A user profile refers to the user's basic information, such as gender, age, and profession. The generated profile has the potential to significantly improve Digital TV (DTV), making the service smarter and more user friendly. We apply the proposed approach to our previous work that introduced decisional DNA TV, which enables TV broadcasters to suggest program choices based upon the user's past viewing habits. Decisional DNA is a domain-independent, flexible, and standard experiential knowledge repository solution that allows for knowledge to be acquired, reused, evolved, and shared in an easy and portable way. The presented conceptual approach demonstrates how fuzzy logic methods can be deployed within DNA TV through an experimental implementation that generates a user profile by capturing viewing habits.

Information Extraction of Review Using LIWC

Social commerce market, which is promoted using a social network service, is getting bigger with the activation of a social network service. If a number of consumers are more than a number which the sellers decide before selling, the deal in social commerce market is completed. It's actually hard to guarantee money back regarding the reason, so reviews are very important to effect for purchasing the products in social commerce market. It may have different direction of information through background, taste, emotion of the reviewers. At the same time, if the meaning of text information can be extracted, we can have the correct information that we need. This paper shows that we extract the meaning by opinion mining and identifying the user's basic information and psychological state by LIWC. It gives the correct ratings to consumers who want to purchase products through social commerce market. Also it can be helpful to increase reliability of opinion mining on the other fields.