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.

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.

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 combining 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 lnterest 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.

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.

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.

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 continuurn. In some sense, specific interests correspond to shortterm interests, while general interests correspond to longterm interests. So this representation more really reflects the users＇ interests. The algorithm can automatically model a us er＇s multiple interest domains, dynamically generate the in terest 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.

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.

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.

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.

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.

基于用户心智模型强化与再构的界面设计流程研究

为了进一步探索心智模型理论,将其作为工具应用到界面设计中,试图探索出一种在用户心智模型引导下的界面设计流程。以心智模型理论为基础,探讨了用户心智模型强化的设计程序与方法、用户心智模型再构的设计程序与方法,并且论述了在界面设计中如何运用用户心智模型强化与再构提升产品界面可用性。总结出基于用户心智模型强化与再构的界面设计流程。通过对3个方面的研究,总结出一种基于用户心智模型强化与再构的界面设计流程,为提升界面信息呈现与用户心智的匹配提供新的设计思路,为产品界面设计提供了新的理论支持。

用户需求视角下医疗健康类APP适老化改造内容优先序研究

[目的/意义]了解用户对医疗健康类APP适老化改造的需求,探析改造内容的优先序,从用户需求视角优化功能设计,为医疗健康类APP适老化的改造提供参考。[方法/过程]在系统梳理相关文献的基础上,运用专家调研法、访谈法等方式提炼26项需求属性,并综合运用Kano模型、混合类分析以及Better-Worse系数分析对归纳的26项需求进行分类,识别各项需求属性的所属类型。[结果/结论]医疗健康类APP适老化改造的用户需求层次分明,分为8项必备型、7项期望型、4项魅力型和7项无差异型,并对此从保障必备型供给、提升期望型质量、扩大魅力型供给和调整无差异型内容4个方面有针对性地提出了对策建议,有效对接用户需求,分层次、有重点地进行医疗健康类APP适老化改造,帮助老年群体更好地融入现代社会。

考虑数据稀疏性的图书推荐协同过滤算法仿真

图书推荐算法易忽略数据稀疏性问题,导致推荐结果与用户感兴趣内容之间存在较大的偏差。在考虑数据稀疏性的基础上提出一种图书推荐协同过滤算法,对数据预处理,通过对用户和用户之间综合信任度分析,利用分布估计算法对用户兴趣建模;构建用户兴趣簇类集,划分用户兴趣,从中选择出与检索对象最接近的邻居;计算邻近项目得分,按照从大到小的顺序排列,排名靠前的资源项即为图书推荐结果。实验结果表明,所提方法在推荐500本图书时,用时在12s内,且降低了平均绝对误差和均方根误差,实现了最精准的图书推荐。

基于KANO和感性工学的电动滑板车设计研究

目的 融合KANO和感性工学两种分析方法,分析用户需求,并根据消费者需求指导设计一款电动滑板车。从而确保滑板车设计方案符合目标用户基本需求,提升用户满意度。方法 通过调查问卷收集消费者对电动滑板车的使用需求,运用KANO进行用户需求分类,构建电动滑板车消费需求评价体系。运用感性工学分析感性要素与电动滑板车设计的影响关系并提取感性词汇。运用SPSS软件对感性词汇进行评价分析,得出符合消费者需求的感性要素辅助设计过程,使设计的电动滑板车更符合用户的需求和心理。结果 根据分析得到电动滑板车用户需求分类,并结合消费感性需求,确定电动滑板车设计方向,以此进行电动滑板车的设计工作。结论 综合应用KANO和感性工学分析电动滑板车的用户使用需求,研究消费者的感性因素,从目标用户出发,这样有助于更加准确地理解消费者需求,洞察滑板车的设计重点,使设计整体过程更加科学、全面。

基于SEM模型的张家苗寨古村落导视系统设计研究

目的改善村寨导视系统的用户体验,探寻用户评价导视系统满意度的标准体系,提供客观、以用户为导向的设计流程。方法使用文本挖掘技术对用户评价原始数据进行采集,使用探索性因子分析法对结果进行降维分析,通过主成分分析法提取5个成分,将其设置为潜在变量,并将相关指标作为观测变量,再使用结构方程模型构建潜在变量与观测变量之间的层次结构,并对其结果进行拟合度分析,最后将结果用于导视系统设计实践。结果根据结构模型结果,分析潜在变量和观测变量的影响权重,得出并验证了“明确的指向性”是影响用户满意度最重要的指标,其次分别为“民族造型美感”“文化功能性”“场景交互合理”,并通过具体的观测变量因子载荷数值排序来指导设计实践。结论导视系统在实现指向性功能的情况下,还应提升民族文化附加值,通过文化符号来扩大民族影响力,传承民族文化,促进民族村寨的发展。