Review of Visualization in User Behavior Analysis

Analyzing user behavior characteristics in a complex power grid environment is essential for user behavior planning and resource coordination optimization. Traditional user behavior analysis methods based on model-driven and causal analysis have the disadvantages of strong subjectivity and physical models that are difficult to deal with the randomness and uncertainty of user behavior in complex grid environments. In this paper, we use unsupervised learning methods to analyze user behavior in complex power grid environments, and propose user behavior analysis methods based on stacked autoencoder and clustering. We first reduce the complexity of user behavior data by proposing adaptive feature selection algorithm of user behavior based on stacked autoencoder and unsupervised learning (AFS-SAEUL). Finally, we build a user behavior analysis model based on adaptive feature selection and improved clustering (UBA-AFSIC). The model improved the performance of unsupervised classification of user behavior by fusing the adaptive generation strategy of the initial cluster centers. The simulation experiment results on two real electricity datasets and one public electric vehicle charging dataset show that compared with the existing feature selection algorithm and clustering algorithm, the algorithms proposed in this paper have higher feature selection rate and better clustering performance.

With the rapid growth of e-commerce, consumer demand for personalized packaging solutions has grown significantly. To address this issue, this article constructs an intelligent recommendation model that combines user behavior data and sentiment analysis techniques. Firstly, a user behavior preference model is established by collecting potential preferences and behavioral characteristics of users, in order to explore their network behavior characteristics. Next, the Bidirectional Encoder Representations from Transformers (BERT) word vector is used to represent the comment text, and a bidirectional recurrent neural network is used to quantify the emotional information in the comment. Based on the emotional rating, the rating matrix is updated to map the shallow features of users and resources. Subsequently, by combining convolutional neural networks and self attention mechanisms, deep features of users and resources are extracted from comment texts, and shallow and deep features are fused through multi-layer neural networks to model the nonlinear interaction between users and resources and predict the rating values of recommended resources. In addition, this article proposes a hierarchical attention enhanced recommendation model incorporating User behavior and sentiment analysis (HAER-UBSA), which obtains feature information from comment texts through attention mechanisms and models comment level embedded representations of users and items. The experimental results show that compared with other baseline models, the mean absolute error (MAE) and root mean squared error (RMSE) indicators of our model have improved by 10.34% and 10.00%, respectively.

This paper presents a continuous authentication system based on user behavior analysis that makes use of environmental context information, users' behavior analysis and Neuro-Fuzzy Logic. This system must be able to acquire information in context, making them into a computational environment. This information is the basis of user behavior. The System, based on the evidences of the behavior, establishes if it can trust the user or not. According to the user behavior, levels of trust are released, to access the application software. Weights are attributed in the fuzzyfication process, according to the rules that were previously established for the parameters which help to establish the evidences of behavioral trust, in its different degrees. The neuro-fuzzy logic allows that the user behavioral database be continuously updated, interacting with the fuzzyfication mechanism, so as to keep trust levels updated according to the user behavior, in a more accurate and faithful way.

Query auto completion (QAC) is a prominent feature in modern search engines. High quality QAC substantially improves search experiences by helping users in typing less while submitting the queries. Many studies have been proposed to improve quality and relevance of the QAC methods from different perspectives, including leveraging contexts in long term and short term query histories, investigating the temporal information for time-sensitive QAC, and analyzing user behaviors. Although these studies have shown the context, temporal, and user behavior data carry valuable information, most existing QAC approaches do not fully exploit or even completely ignore these information. We propose a novel Hawkes process based QAC algorithm, comprehensively taking into account the context, temporal, and position of the clicked recommended query completions (a type of user behavior data), for reliable query completion prediction. Our understanding of ranking query completions is consistent with the mathematical rationale of Hawke process; such a coincidence in turn validates our motivation of using Hawkes process for QAC. We also develop an efficient inference algorithm to compute the optimal solutions of the proposed QAC algorithm. The proposed method is evaluated on two real-world benchmark data in comparison with state-of-art methods, and the obtained experiments clearly demonstrate their effectiveness.

Cyber criminals utilize compromised user accounts to gain access into otherwise protected systems without the need for technical exploits. User and Entity Behavior Analytics (UEBA) leverages anomaly detection techniques to recognize such intrusions by comparing user behavior patterns against profiles derived from historical log data. Unfortunately, hardly any real log data sets suitable for UEBA are publicly available, which prevents objective comparison and reproducibility of approaches. Synthetic data sets are only able to alleviate this problem to some extent, because simulations are unable to adequately induce the dynamic and unstable nature of real user behavior in generated log data. We therefore present a real system log data set from a cloud computing platform involving more than 5000 users and spanning over more than five years. To evaluate our data set for the scenario of account hijacking, we outline a method for attack injection and subsequently disclose the resulting manifestations with an adaptive anomaly detection mechanism.

Abstract: Phishing attacks remain a persistent threat in cyberspace, demanding innovative solutions for timely detection. In this paper, HyperFusion, a pioneering approach that combines machine learning with user behavior analysis, is introduced to enhance the real-time identification of phishing websites. By integrating features derived from URL and hyperlink characteristics with dynamic user interaction patterns, HyperFusion achieves unprecedented accuracy without reliance on thirdparty systems. Traditional anti-phishing methods often falter against zero-hour attacks or novel phishing websites, underscoring the need for novel strategies. The methodology addresses this challenge by leveraging user behavior data alongside client-side information, thereby eliminating external dependencies and fortifying defense mechanisms. Additionally, a tailored dataset for rigorous experimentation is presented, facilitating a comprehensive evaluation of the approach. Experimental results demonstrate HyperFusion's superior efficacy, boasting a remarkable detection accuracy of 99.37% with the XG Boost technique.

This article explores the applications of artificial intelligence in the field of user behavior analysis on websites and the potential of this technology. Artificial intelligence algorithms are widely used in various fields such as personalized content presentation by analyzing user behavior, enhancing customer experience through chatbots and virtual assistants, improving user experience, optimizing marketing campaigns, increasing customer satisfaction, and detecting fraudulent activities. The article explains how various technologies of artificial intelligence, including methods such as machine learning, deep learning, and natural language processing, are used to predict and analyze the behavior of website users. It also discusses the potential opportunities of artificial intelligence for more accurate analysis and prediction of user behavior in the future. The article highlights the growing role of artificial intelligence in user behavior analysis and shows what strategic advantages advances in this field provide for businesses.

As computer and network intrusions become more and more of a concern, the need for better capabilities to assist in the detection and analysis of intrusions also increases. We propose a methodology for analyzing network and computer log information visually based on the analysis of user behavior. Each user's behavior is the key to determining their intent and overriding goals, whether they attempt to hide their actions or not. Proficient hackers will attempt to hide their ultimate goal, which hinders the reliability of log file analysis. Visually analyzing the user's behavior, however, is much more adaptable and difficult to counteract. This paper will discuss how user behavior can be exhibited within the visualization techniques, the capabilities provided by the environment, typical characteristics users should look out for (i.e., how unusual behavior exhibits itself), and exploration paradigms effective for identifying the meaning behind the user's behavior.

The analysis of user behavior in online communities is a prominent topic in social media research. As such, user behavior is often analyzed using a set of metrics that describe the user’s participation behavior and structural position in the social network. Yet, for Enterprise Social Networks (ESN), i.e. internally used social networking platforms, such research is lacking. While prior studies have found users to engage in knowledge-intensive interactions, e.g. discussing and developing new ideas, little is known about how to conceptualize and measure ESN user behavior. Being able to measure user behavior, however, is an important prerequisite for the identification of knowledge management-related roles in the context of ESN.

Modern mass customization production allows user interaction activities to be distributed in the full product life cycle via multiple information systems. Investigating user behaviors across the boundaries of different domains helps to deeply integrate isolated fragmental profiles into a comprehensive one, and therefore can provide multi-dimensional, high-quality and valuable services. However, traditional user behavior analysis models are based on individual user profile information derived from separate domains, such as requirement analysis, design, supply chain, logistics, marketing, etc., which have not considered the whole complexity of mass customization manufacturing. In this paper, we introduce the concept of multi-dimensional semantic activity space, where user behavior features are merged and represented as combined vectors. User behavior patterns are discovered by mining action data extracted from log files in different subsystems in the corresponding domains. We also identify distinct categories of user behaviors in various modules and subsystems in the context of an intelligent manufacturing environment. Experiment results show a strong indication that the proposed approach can be applied to reveal variations in typical behavioral aspects of cross-domain participants, in terms of patterns in resource access, operation tasks, performance assessment, etc.

Japanese language instruction at universities has gained new life thanks to the quick growth of online learning via the Internet, microclasses, flipped classrooms, and other innovative teaching methods. This is the direction of future educational reform in colleges and universities. After gathering and pre-processing behavioral data of Japanese learners, this study builds a data analysis model of Japanese online learning user behavior based on the Japanese online education platform. In this model, user behavioral features are extracted and classified using the RFG-SVM model, which is based on SVM. Users with similar user behaviors are then clustered together using the FCM algorithm, and the association rule algorithm is utilized to explore the intricate relationship between user online learning behaviors and learning effects. Lastly, the FSQCA approach is used to investigate the optimization path of Japanese online education platforms after combining with example analysis. The most significant aspect of Japanese online learning is its online learning rate (0.7499). Users can be categorized into three groups: close cooperation (52.3%), active participation (6.1%), and weak participation (41.6%). Japanese online learners also exhibit better user behavior. The consistency indexes for Grouping H1: SI*LA*CC*HA, Grouping H2: ~SQ*CQ*SI*~LA*CC, and Grouping H3: SQ*CQ*SI*~LA*HA were 0.929, 0.959, and 0.965, respectively, with both social influence (SI) and habit (HA) serving as important requisites. This study helps to form a mature mechanism that prompts Japanese online education users to develop a continuous willingness to use the program, which contributes to the development of inclusive education to a certain extent.

Haptic technology has the potential to enhance education, especially for those with severe visual impairments (those that are blind or who have low vision), by presenting abstract concepts through the sense of touch. Despite the advances in haptic research, little research has been conducted in the area of haptic user behavior toward the establishment of haptic interface development and design conventions. To advance haptic research closer to this goal, this study examines haptic user behavior data collected from 9 participants utilizing a haptic learning system, the Heat Temperature Module. ANOVA results showed that differences in the amount of haptic feedback result in significant differences in user behavior, indicating that higher levels of haptic friction feedback result in higher user interaction proportions of data. Results also suggested that minimal thresholds of friction haptic feedback can be established for a desired level of minimum user interaction data proportions, however; more research is needed to establish such thresholds.KeywordsHaptic User InterfaceThermal DeviceUser BehaviorInclusive Design

In China, the Internet has developed to a relatively mature scale, and Internet applications have gradually transitioned from being singular to diversified. The Internet is changing people's ways of learning, working, and living, and even influencing the progress of the entire society. Against the backdrop of rapid Internet development, we have gradually entered the "big data era." Faced with such a vast amount of data, single-machine statistics have become inadequate. This paper first introduces the background and significance of the research, providing a preliminary description of network traffic from the perspective of network services. It then elaborates on the concepts and classifications of network user behavior, along with key data, mainly introducing analysis methods. The primary method used in this paper is cluster analysis, including distance and similarity coefficients in cluster analysis, with a focus on the main steps and algorithm process of k-means clustering. Finally, the paper conducts user behavior analysis based on the clustering results.

Classification of the Functions of Urban Area is of great significance to urban construction and refined management. High-resolution remote sensing images are widely used in urban area functional classification, and urban area functions are closely related to human activities. Therefore, in the urban area functional classification model, the effects of remote sensing images and user behavior will be significantly improved. Firstly, the remote sensing image acquired by the satellite is random. We need to delete the all black or all white image and then perform the defogging operation to make the feature more obvious; Secondly, the user behavior data is processed as a three-dimensional image according to the divided time; Finally, the densenet, resnet, se_resnext, inception network models are integrated to extract image data and text data for classification. The experimental results show that compared with other methods, our proposed method has a good effect on the classification of the functions of urban area which based on remote sensing image data and user behavior data.

Social media provides valuable resources to analyze user behaviors and capture user preferences. This article focuses on analyzing user behaviors in social media systems and designing a latent class statistical mixture model, named temporal context-aware mixture model (TCAM), to account for the intentions and preferences behind user behaviors. Based on the observation that the behaviors of a user in social media systems are generally influenced by intrinsic interest as well as the temporal context (e.g., the public's attention at that time), TCAM simultaneously models the topics related to users' intrinsic interests and the topics related to temporal context and then combines the influences from the two factors to model user behaviors in a unified way. Considering that users' interests are not always stable and may change over time, we extend TCAM to a dynamic temporal context-aware mixture model (DTCAM) to capture users' changing interests. To alleviate the problem of data sparsity, we exploit the social and temporal correlation information by integrating a social-temporal regularization framework into the DTCAM model. To further improve the performance of our proposed models (TCAM and DTCAM), an item-weighting scheme is proposed to enable them to favor items that better represent topics related to user interests and topics related to temporal context, respectively. Based on our proposed models, we design a temporal context-aware recommender system (TCARS). To speed up the process of producing the top- k recommendations from large-scale social media data, we develop an efficient query-processing technique to support TCARS. Extensive experiments have been conducted to evaluate the performance of our models on four real-world datasets crawled from different social media sites. The experimental results demonstrate the superiority of our models, compared with the state-of-the-art competitor methods, by modeling user behaviors more precisely and making more effective and efficient recommendations.