Framework to Model User Request Access Patterns in the World Wide Web

In this paper, we present a novel approach to model user request patterns in the World Wide Web. Instead of focusing on the user traffic for web pages, we capture the user interaction at the object level of the web pages. Our framework model consists of three sub-models: one for user file access, one for web pages, and one for storage servers. Web pages are assumed to consist of different types and sizes of objects, which are characterized using several categories: articles, media, and mosaics. The model is implemented with a discrete event simulation and then used to investigate the performance of our system over a variety of parameters in our model. Our performance measure of choice is mean response time and by varying the composition of web pages through our categories, we find that our framework model is able to capture a wide range of conditions that serve as a basis for generating a variety of user request patterns. In addition, we are able to establish a set of parameters that can be used as base cases. One of the goals of this research is for the framework model to be general enough that the parameters can be varied such that it can serve as input for investigating other distributed applications that require the generation of user request access patterns.

The User Interfaces Transition Diagram-Editor: A Tool to Simplify User-System Interaction Modeling

The User Interface Transition Diagram (UITD) is a formal modeling notation that simplifies the specification and design of user-system interactions. It is a valuable communication tool for technical and non-technical stakeholders during the requirements elicitation phase, as it provides a simple yet technically complete notation that is easy to understand. In this paper, we investigated the efficiency of creating UITDs using draw.io, a widely used diagramming software, compared to a dedicated UITD editor. We conducted a study to compare the time required to use each tool to complete the task of creating a medium size UITD, as well as the subjective ease of use and satisfaction of participants with the dedicated Editor. Our results show that the UITD editor is more efficient and preferred by participants, highlighting the importance of using specialized tools for creating formal models such as UITDs. The findings of this study have implications for software developers, designers, and other stakeholders involved in the specification and design of user-system interactions.

Mining User Interest in Microblogs with a User-Topic Model

Microblogs have become an important platform for people to publish,transform information and acquire knowledge.This paper focuses on the problem of discovering user interest in microblogs.In this paper,we propose a topic mining model based on Latent Dirichlet Allocation(LDA) named user-topic model.For each user,the interests are divided into two parts by different ways to generate the microblogs:original interest and retweet interest.We represent a Gibbs sampling implementation for inference the parameters of our model,and discover not only user's original interest,but also retweet interest.Then we combine original interest and retweet interest to compute interest words for users.Experiments on a dataset of Sina microblogs demonstrate that our model is able to discover user interest effectively and outperforms existing topic models in this task.And we find that original interest and retweet interest are similar and the topics of interest contain user labels.The interest words discovered by our model reflect user labels,but range is much broader.

Direct immune-SCIR public-opinion propagation model based on real-time online users

Current public-opinion propagation research usually focused on closed network topologies without considering the fluctuation of the number of network users or the impact of social factors on propagation. Thus, it remains difficult to accurately describe the public-opinion propagation rules of social networks. In order to study the rules of public opinion spread on dynamic social networks, by analyzing the activity of social-network users and the regulatory role of relevant departments in the spread of public opinion, concepts of additional user and offline rates are introduced, and the direct immune-susceptible, contacted, infected, and refractory (DI-SCIR) public-opinion propagation model based on real-time online users is established. The interventional force of relevant departments, credibility of real information, and time of intervention are considered, and a public-opinion propagation control strategy based on direct immunity is proposed. The equilibrium point and the basic reproduction number of the model are theoretically analyzed to obtain boundary conditions for public-opinion propagation. Simulation results show that the new model can accurately reflect the propagation rules of public opinion. When the basic reproduction number is less than 1, public opinion will eventually disappear in the network. Social factors can significantly influence the time and scope of public opinion spread on social networks. By controlling social factors, relevant departments can analyze the rules of public opinion spread on social networks to suppress the propagate of negative public opinion and provide a powerful tool to ensure security and stability of society.

User growth and penetration modeling for converged heterogeneous network

To evaluate the trail potential of converged heterogeneous network( CHN) market,the logistic method for adoption modeling of CHN is used. User growth & penetration have been taken as two variants to find saturation condition in market. Model is continuous in time but modifications are done for discrete recurrence equation,commonly known as logistic map. Dynamic and static phases are taken into consideration while penetration decay is not covered in this model.

Blog acceptance model: An empirical study on exploring users' acceptance and continual usage of blogs

Blogs have permeated into our daily lives at a fast speed,and various kinds of blog spaces have attracted our attention.However,little effort has been made on studying the users’motivation to participate in blog activities.This paper aims to construct a theoretical model about the blog adoption based on technology acceptance model(TAM theory),social capital theory and social exchange theory,and put forward 18 related hypotheses.Then the survey method is adopted to analyze the data from 208 questionnaires using the SPSS and LISREL tools,and to examine the theoretical model and hypotheses.Finally,the paper makes a discussion from five aspects due to the results of data analysis,including individual driving factors,group driving factors,community driving factors,technology acceptance factors and moderating variables.The results show that curiosity/enjoyment,user’s experience,social interaction and social identification will greatly affect users’motivation to accept a blog;meanwhile,perceived ease of use,exchange cost and trust will partially influence users’intention to participate in blog activities.The results also suggest that age and education degrees have significant moderating effects on users’acceptance and updating of blogs.

Research on the User Interest Modeling of Personalized Search Engine

At present,how to enable Search Engine to construct user personal interest model initially,master user's personalized information timely and provide personalized services accurately have become the hotspot in the research of Search Engine area. Aiming at the problems of user model's construction and combin-ing techniques of manual customization modeling and automatic analytical modeling,a User Interest Model(UIM) is proposed in the paper. On the basis of it,the corresponding establishment and update algorithms of User Interest Profile(UIP) are presented subsequently. Simulation tests proved that the UIM proposed and corresponding algorithms could enhance the retrieval precision effectively and have superior adaptability.

The Impact of User and Traffic Models on the Design of the Communications Network in the Smart Grid

A critical component of the smart grid (SG) infrastructure is the embedded communications network, where an important objective of the latter is the expansion of its throughput, in conjunction with the satisfaction of specified latency and accuracy requirements. For the effective design of the communications network, the user and traffic profiles, such as known-user vs. unknown-user populations and bursty vs. non-bursty data traffics, must be carefully considered and subsequently modeled. This paper relates user and traffic models to the deployment of effective multiple access transmission algorithms in the communications network of the SG.

User’s Relevance of PIR System Based on Cloud Models

A new method to evaluate fuzzily user’s relevance on the basis of cloud models has been proposed. All factors of personalized information retrieval system are taken into account in this method. So using this method for personalized information retrieval (PIR) system can efficiently judge multi-value relevance, such as quite relevant, comparatively relevant, commonly relevant, basically relevant and completely non-relevant, and realize a kind of transform of qualitative concepts and quantity and improve accuracy of relevance judgements in PIR system. Experimental data showed that the method is practical and valid. Evaluation results are more accurate and approach to the fact better.

A Multi-Agent Based Model for User Interest Mining on Sina Weibo

User interest mining on Sina Weibo is the basis of personalized recommendations,advertising,marketing promotions,and other tasks.Although great progress has been made in this area,previous studies have ignored the differences among users:the varied behaviors and habits that lead to unique user data characteristics.It is unreasonable to use a single strategy to mine interests from such varied user data.Therefore,this paper proposes an adaptive model for user interest mining based on a multi-agent system whose input includes self-descriptive user data,microblogs and correlations.This method has the ability to select the appropriate strategy based on each user’s data characteristics.The experimental results show that the proposed method performs better than the baselines.

A Structural Topic Model for Exploring User Satisfaction with Mobile Payments

This study explored user satisfaction with mobile payments by applying a novel structural topic model.Specifically,we collected 17,927 online reviews of a specific mobile payment(i.e.,PayPal).Then,we employed a structural topic model to investigate the relationship between the attributes extracted from online reviews and user satisfaction with mobile payment.Consequently,we discovered that“lack of reliability”and“poor customer service”tend to appear in negative reviews.Whereas,the terms“convenience,”“user-friendly interface,”“simple process,”and“secure system”tend to appear in positive reviews.On the basis of information system success theory,we categorized the topics“convenience,”“user-friendly interface,”and“simple process,”as system quality.In addition,“poor customer service”was categorized as service quality.Furthermore,based on the previous studies of trust and security,“lack of reliability”and“secure system”were categorized as trust and security,respectively.These outcomes indicate that users are satisfied when they perceive that system quality and security of specific mobile payments are great.On the contrary,users are dissatisfied when they feel that service quality and reliability of specific mobile payments is lacking.Overall,our research implies that a novel structural topic model is an effective method to explore mobile payment user experience.

User Model Clustering

User model which is the representation of information about user is the heart of adaptive systems. It helps adaptive systems to perform adaptation tasks. There are two kinds of adaptations: 1) Individual adaptation regarding to each user;2) Group adaptation focusing on group of users. To support group adaptation, the basic problem which needs to be solved is how to create user groups. This relates to clustering techniques so as to cluster user models because a group is considered as a cluster of similar user models. In this paper we discuss two clustering algorithms: k-means and k-medoids and also propose dissimilarity measures and similarity measures which are applied into different structures (forms) of user models like vector, overlay, and Bayesian network.

UMIS: A Service for User Model Interoperability

In this paper we describe UMIS, a service architecture that enables user adaptive applications to exchange User Model data on the Web. UMIS provides a set of facilities that allow applications to interoperate with minimum changes in their internal logics and knowledge representation. The goal is to support the process of interoperability in three ways: providing an efficient centralized discovery service;offering a service for simple interaction for the exchange of UM value in a p2p way;and offering a negotiation mechanism to be used in case of communication hurdles (i.e. semantic ambiguities and missing response). We developed a proof-of-concept prototype of UMIS and we tested it with an existing user-adaptive application. According to our test results, our approach improves the communication with respect to standard solutions for interoperability regarding the quality of exchange, with a negligible impact on the communication costs and traffic generation.

User satisfaction-based quality evaluation model and survey analysis of network information service

On the basis of user satisfaction,authors made research hypotheses by learning from relevant e-service quality evaluation models.A questionnaire survey was then conducted on some content-based websites in terms of their convenience,information quality,personalization and site aesthetics,which may affect the overall satisfaction of users.Statistical analysis was also made to build a user satisfaction-based quality evaluation system of network information service.

Learning Hierarchical User Interest Models from Web Pages

We propose an algorithm for learning hierarchical user interest models according to the Web pages users have browsed. In this algorithm, the interests of a user are represented into a tree which is called a user interest tree, the content and the structure of which can change simultaneously to adapt to the changes in a user’s interests. This expression represents a user’s specific and general interests as a continuum. In some sense, specific interests correspond to short-term interests, while general interests correspond to long-term interests. So this representation more really reflects the users’ interests. The algorithm can automatically model a user’s multiple interest domains, dynamically generate the interest models and prune a user interest tree when the number of the nodes in it exceeds given value. Finally, we show the experiment results in a Chinese Web Site.

Personalized Web Image Retrieval Based on User Interest Model

The traditional search engines don’t consider that the users interest are different, and they don’t provide personalized retrieval service, so the retrieval efficiency is not high. In order to solve the problem, a method for personalized web image retrieval based on user interest model is proposed. Firstly, the formalized definition of user interest model is provided. Then the user interest model combines the methods of explicit tracking and implicit tracking to improve user’s interest information and provide personalized web image retrieval. Experimental results show that the user interest model can be successfully applied in web image retrieval.

Agent Modeling of User Preferences Based on Fuzzy Classified ANNs in Automated Negotiation

In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated transactions.The difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic variability.In this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user preferences.The refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be achieved.Compared to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.

元宇宙场域下的公共图书馆用户空间服务需求

元宇宙相关技术的应用与发展是推进图书馆服务变革与优化的重要机遇,为多元空间建设提供方向,对公共图书馆空间服务创新具有指导意义。文章对不同年龄、学历、专业以及地域的公共图书馆用户进行深度访谈,通过扎根理论研究挖掘用户对公共图书馆的空间服务需求,结合KANO模型构建元宇宙场域下公共图书馆用户空间服务需求模型,进而提出发展策略。结果表明:图书馆管理是用户空间服务需求的外部驱动因素,用户体验与空间配置是用户空间服务需求的内部驱动因素。公共图书馆应以智能化为目标、以多元治理为手段、以技术为依托,从图书馆建设、用户需求以及空间配置三个方面提升空间服务能力。