With the rapid development of science and technology and the increasing popularity of the Internet,the number of network users is gradually expanding,and the behavior of network users is becoming more and more complex...With the rapid development of science and technology and the increasing popularity of the Internet,the number of network users is gradually expanding,and the behavior of network users is becoming more and more complex.Users’actual demand for resources on the network application platform is closely related to their historical behavior records.Therefore,it is very important to analyze the user behavior path conversion rate.Therefore,this paper analyses and studies user behavior path based on sales data.Through analyzing the user quality of the website as well as the user’s repurchase rate,repurchase rate and retention rate in the website,we can get some user habits and use the data to guide the website optimization.展开更多
Based on perceptual control theory,a task analysis approach is proposed to describe more accurately user tasks in dynamic environments,which is of more powerful and flexible descriptive ability. Theoretically,a task m...Based on perceptual control theory,a task analysis approach is proposed to describe more accurately user tasks in dynamic environments,which is of more powerful and flexible descriptive ability. Theoretically,a task meta model is established to describe the interactive process in an individual,dynamic,and flexible way.Methodologically,an implementation framework is illustrated to map the user-oriented description into implementation-oriented models,which will be as a technical tool to transform from a task model to a user interface prototype.展开更多
Recommendation systems have been extensively studied over the last decade in various domains. It has been considered a powerful tool for assisting business owners in promoting sales and helping users with decision-mak...Recommendation systems have been extensively studied over the last decade in various domains. It has been considered a powerful tool for assisting business owners in promoting sales and helping users with decision-making when given numerous choices. In this paper, we propose a novel Graph-based Context-Aware Recommendation Systems with Knowledge Graph to analyse and predict users’ behaviours, i.e., making recommendations based on historical events and their implicit associations. The model incorporates contextual information extracted from both users’ historical behaviours and events relations, where the contexts have been modelled as knowledge graphs. By leveraging the advantages offered from the knowledge graph, events dependencies and their subtle relations can be established and have been introduced in the recommendation process. Experimental results indicate that the proposed approach can outperform the state-of-the-art algorithms and achieve more accurate recommendations.展开更多
The rapid adoption of online social media platforms has transformed the way of communication and interaction.On these platforms,discussions in the form of trending topics provide a glimpse of events happening around t...The rapid adoption of online social media platforms has transformed the way of communication and interaction.On these platforms,discussions in the form of trending topics provide a glimpse of events happening around the world in real-time.Also,these trends are used for political campaigns,public awareness,and brand promotions.Consequently,these trends are sensitive to manipulation by malicious users who aim to mislead the mass audience.In this article,we identify and study the characteristics of users involved in the manipulation of Twitter trends in Pakistan.We propose“Manipify”-a framework for automatic detection and analysis of malicious users in Twitter trends.Our framework consists of three distinct modules:(1)user classifier,(2)hashtag classifier,and(3)trend analyzer.The user classifier module introduces a novel approach to automatically detect manipulators using tweet content and user behaviour features.Also,the module classifies human and bot users.Next,the hashtag classifier categorizes trending hashtags into six categories assisting in examining manipulators behaviour across different categories.Finally,the trend analyzer module examines users,hashtags,and tweets for hashtag reach,linguistic features,and user behaviour.Our user classifier module achieves 0.92 and 0.98 accuracy in classifying manipulators and bots,respectively.We further test Manipify on the dataset comprising 652 trending hashtags with 5.4 million tweets and 1.9 million users.The analysis of trends reveals that the trending panel is mostly dominated by political hashtags.In addition,our results show a higher contribution of human accounts in trend manipulation as compared to bots.展开更多
基金funded by the Open Foundation for the University Innovation Platform in the Hunan Province,grant number 18K103Open project,Grant Number 20181901CRP03,20181901CRP04,20181901CRP05+1 种基金Hunan Provincial Education Science 13th Five-Year Plan(Grant No.XJK016BXX001),Social Science Foundation of Hunan Province(Grant No.17YBA049)supported by the project 18K103。
文摘With the rapid development of science and technology and the increasing popularity of the Internet,the number of network users is gradually expanding,and the behavior of network users is becoming more and more complex.Users’actual demand for resources on the network application platform is closely related to their historical behavior records.Therefore,it is very important to analyze the user behavior path conversion rate.Therefore,this paper analyses and studies user behavior path based on sales data.Through analyzing the user quality of the website as well as the user’s repurchase rate,repurchase rate and retention rate in the website,we can get some user habits and use the data to guide the website optimization.
基金Supported by the National Natural Science Foundation of China(61272286)the Specialized Research Fund for the Doctoral Program of Higher Education of China(20126101110006)
文摘Based on perceptual control theory,a task analysis approach is proposed to describe more accurately user tasks in dynamic environments,which is of more powerful and flexible descriptive ability. Theoretically,a task meta model is established to describe the interactive process in an individual,dynamic,and flexible way.Methodologically,an implementation framework is illustrated to map the user-oriented description into implementation-oriented models,which will be as a technical tool to transform from a task model to a user interface prototype.
文摘Recommendation systems have been extensively studied over the last decade in various domains. It has been considered a powerful tool for assisting business owners in promoting sales and helping users with decision-making when given numerous choices. In this paper, we propose a novel Graph-based Context-Aware Recommendation Systems with Knowledge Graph to analyse and predict users’ behaviours, i.e., making recommendations based on historical events and their implicit associations. The model incorporates contextual information extracted from both users’ historical behaviours and events relations, where the contexts have been modelled as knowledge graphs. By leveraging the advantages offered from the knowledge graph, events dependencies and their subtle relations can be established and have been introduced in the recommendation process. Experimental results indicate that the proposed approach can outperform the state-of-the-art algorithms and achieve more accurate recommendations.
基金This work was supported by Higher Education Commission(HEC)Pakistan and Ministry of Planning Development and Reforms under National Center in Big Data and Cloud Computing.
文摘The rapid adoption of online social media platforms has transformed the way of communication and interaction.On these platforms,discussions in the form of trending topics provide a glimpse of events happening around the world in real-time.Also,these trends are used for political campaigns,public awareness,and brand promotions.Consequently,these trends are sensitive to manipulation by malicious users who aim to mislead the mass audience.In this article,we identify and study the characteristics of users involved in the manipulation of Twitter trends in Pakistan.We propose“Manipify”-a framework for automatic detection and analysis of malicious users in Twitter trends.Our framework consists of three distinct modules:(1)user classifier,(2)hashtag classifier,and(3)trend analyzer.The user classifier module introduces a novel approach to automatically detect manipulators using tweet content and user behaviour features.Also,the module classifies human and bot users.Next,the hashtag classifier categorizes trending hashtags into six categories assisting in examining manipulators behaviour across different categories.Finally,the trend analyzer module examines users,hashtags,and tweets for hashtag reach,linguistic features,and user behaviour.Our user classifier module achieves 0.92 and 0.98 accuracy in classifying manipulators and bots,respectively.We further test Manipify on the dataset comprising 652 trending hashtags with 5.4 million tweets and 1.9 million users.The analysis of trends reveals that the trending panel is mostly dominated by political hashtags.In addition,our results show a higher contribution of human accounts in trend manipulation as compared to bots.
