User Behavior Analysis by Cross-Domain Log Data Fusion

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.

User behavior visualization as a powerful tool can help analysts better understand user behavior data. Previous research such as Information Architecture believes that a user journey map is the regular way to visualize user behavior patterns. The technique is a diagram that shows the steps, touch-points, and emotions that users go through when using their products or services. Current studies mainly focus on the procedure and framework of applying user behavior data into visualization tools and its benefit to business development. Studies also found that visualization in user behavior analysis has difficulty in dealing with complex data. However, few studies have discussed the possible solution for the current limitation of visualization in user behavior analysis. With the popularization of the Internet and mobile devices, the scale and scale of user behavior data and the complexity will continue to increase, and user behavior visualization will be an important tool for analyzing this data, helping to tap potential business opportunities and improve the efficiency of data analysis. Therefore, we would make the review on visualization in user behavior analysis. We will first provide the background of the user behavior and its visualization and then introduce the definition of user behavior. Afterward, we will discuss the current process of visualization in user behavior analysis and its current limitations. Next, we will propose the possible solution of introducing machine learning, deep learning, predicting the data with a sequence model, and using cluster analysis with usability analysis tools to contribute to the future development of visualization in user behavior analysis. We believe that with data amount and scale keep increasing, the visualization tools need to develop themselves in diversity and data processing speed.

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.

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.

Finding prevalent mobile user patterns in large amount of data has been one of the major problems in the area of mobile data mining. Particularly, the algorithms of discovering frequent user's behavior patterns in the mobile agent system have been studied extensively in recent years. The key feature in most of these algorithms is that they use a location log dataset with userpsilas requested services. In this case, some problems occur because they do not consider that mobile user's behavior patterns are dynamically variable as time passes. In this paper, we propose a novel data mining method based on temporal mobile access patterns that can efficiently discover mobile user's behavior patterns. An advantage of our method compactly stores user's behavior patterns according to location log dataset and requested services in memory. Thus, even the information datasets require large shared memory when they store, our approach still provides faster access and consume less memory than existing techniques.

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.

Characteristics of the information flow of Mass Customization supply chain is the focus of this study. The MC supply chain has its own characteristics and the flow of information in MC supply chain could improve and be optimized when compared with traditional production mode. This paper summarizes the information flow path of the MC supply chain and emphasizes the importance of information sharing. The Information Center model of information sharing could better fits characteristics of MC supply chain. In 1987, Stan Davis first introduced the concept of Mass Customization in Future Perfect. In 1993, in the Mass Customization: the New Future in Business Competition, Joseph. Pine II demonstrated the effectiveness of Mass Customization production mode (Joseph 1993). The writer of Agile Product Development for Mass Customization,David. M. Anderson considered that the transition from Mass Production to Mass Customization is inevitable and emphasized the importance of the information flow in MC (David 1999).

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.

Users for online shopping are increasing day by day because of easy to get and time-saving property of online shopping. Having a proper understanding of users interest for certain type of product or different products for online shopping becomes important to create personalized service for a target market. An important property of successful e-commerce website is the ability to provide useful content at the right time to users. And because of all this, personalization techniques are introduced to create adaptive shopping application in which user interfaces change according to users interest. User’s behavior information is stored in web log files, and to get the information data mining techniques are used in which they use statistical characters to model users behavior and not considering the sequence of action performed by uses. It becomes helpful if we follow user’s session to understand complex user behavior. Therefore to eliminates all these issues this paper proposes a linear-temporal logic model checking approach for the analysis of structured e-commerce weblogs. If we consider a common way of mapping log records according to e-commerce structure, weblogs can be easily converted into event logs by which behavior of the user is captured. After getting users behavior by performing different predefine queries to identify different behavioral patterns that consider the different actions performed by a user during a session.

At home and abroad, smart home, after more than ten years development is getting more and more attention and favor. With the rapid development of smart home, a huge amount of life data have been accumulated, and these data include a lot of information about user behavior, they can reflect the user's activity truly and objectively, but these data exist in different data sources, such as computers, smart phones, smart televisions and so on. Most of the current user behavior analyses are aimed at single data source, so it is difficult to meet the demand for isomerization data. This paper targets at isomerization data from different data sources, and makes the right model to find out the characteristics of user behavior.

Aiming at the data security problem of the system in the network environment, this paper uses the hidden Markov model to analyze the user's access behavior in the network system and identify the malicious user behavior. First, the behavior characteristics of network users are analyzed, and then according to the related characteristics of hidden Markov, the observation order symbol and hidden state in the model are determined, and the user's access behavior is mapped into a hidden Markov chain. We give detailed experiments to prove the feasibility of applying the hidden Markov model to network user behavior authentication, and compare with other methods to get the accuracy of the model.

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.

Social media has developed rapidly, which had already affected modern people's life style. Meanwhile, social media has produced huge amounts of data. In research, we find that there is no explicit definition of user's behavior pattern in social network. Therefore, we focused on the research of that and found people who have two types of behaviors which are inseparably intertwined. The two types that mentioned are functional behaviors and social behaviors. We have defined user's behavior, and regarded user's behavior as a vertical timeline which the object of study is individual. With adding some horizontal lines which stand for social behaviors between vertical timelines, the model linked different people. Experiment's research takes Sina Weibo as an object. For obtaining the social behavior pattern, we use the generalized sequential pattern algorithm (GSP) for data mining. And the pattern is represented by the form of [User Behavior User a#x2192; User Behavior User]*n. According to the result and algorithm accuracy confirmation, the frequent behavioral patterns of a certain person can be obtained. After that we can also predict user's behaviors in the future.

Online video service has become prevalent in recent years. A better understanding of the user behavior of such service is crucial for allocating the network resources and adjusting the service design. While there are some measurement studies on the non-mobile video services in fixed network, the usage of video service in mobile network is yet to be explored. In this paper, we present a detailed analysis of the user behavior characteristics of a leading comprehensive online video service, namely Youku, in cellular network. This paper is based on a large-scale data set containing over 17 billion traffic traces, collected from a major cellular network in Northeastern China. We analyze the user behavior from three key aspects: data consumption, service usage, and mobility. We provide an insight into how the mobile video service is utilized by users (especially the heavy users), by measuring the user intensities and various representative behavior features in each analysis aspect, such as active time, replay rate, video category, access location, and residence time. We reveal the patterns of different user behaviors, and discuss the implications for practical application. The findings of this paper can provide direct help for network operators and service providers to improve the network performance and user experience.

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