基于用户满意度的学习服务发现算法

针对用户对e-Learning服务发现系统提供的服务不满意或者满意程度不稳定的问题,引入了用户满意度因子,设计了一个学习服务发现算法--eLSDAUS.该算法允许用户参与服务发现的过程,对服务发现的效果进行评价,学习服务发现系统把用户的评价反馈到学习服务发现算法,利用修正函数修正更新发布服务各属性的匹配度权值,优化反馈给用户的综合匹配度的计算,实验表明,在发布的学习服务数量超过1万时,该算法能够提高服务发现的查准率3%,而且随着发布服务数量的增多,效果会更好,经过127天的学习,用户对服务发现结果的总体满意比率可超过93%.     

基于用户满意度的学习服务发现算法

引入用户满意度因子,设计一个学习服务发现算法——eLSDAus,并应用于网络学习系统中。该算法允许用户参与服务发现的过程,对服务发现的效果进行评价。学习服务发现系统将用户评价反馈到学习服务发现算法,利用修正函数修正更新发布服务各属性的匹配度权值,优化反馈给用户的综合匹配度的计算。实验结果表明,在发布的学习服务数量超过1万时,该算法能提高服务发现的查全率4％～5％。网络学习者使用该系统7天后,对学习服务发现结果的总体满意比率可达到93％以上。     

基于分布式学习自动机和用户反馈的网页排序算法研究

为了改善传统PageRank算法存在的不足,例如平分链接权重、主题漂移和忽略用户兴趣,提出一种基于分布式学习自动机和用户反馈的网页排序算法。利用页面内容的相似性、网页之间的超链接和用户遍历的路径,根据分布式学习自动机来确定网页间的超链接权重。考虑到用户反馈包含大量的价值信息,选择用户的转载、回复以及有效点击特征作为用户的行为特征,获得用户反馈因子。根据网页间的超链接权重和用户反馈因子计算每个网页的排名。仿真实验表明,与传统的PageRank算法和WPR算法相比,该算法在一定程度上提高了信息检索的精准度和用户满意度。     

基于增量学习的混合推荐算法

推荐系统是自适应信息系统中的个性化服务模块,可以根据目标用户的信息需求提供个性化的信息服务。针对传统协作过滤算法存在的用户兴趣描述粒度过大问题,以及稀疏评分矩阵造成相似度计算不准确的问题,提出了一种基于增量学习的混合推荐算法WHHR,该算法通过Widrow-Hoff增量学习构建基于内容的用户模型,并结合协作过滤推荐机制实现评分预测。实验验证了WHHR算法在收敛速度和推荐准确性方面较类似推荐算法有较大提高。     

基于搜索历史的用户兴趣模型的研究

提出了一种新的基于搜索历史的用户兴趣模型，目的是解决现有搜索引擎很难考虑用户兴趣来实现用户个性化搜索以及用户兴趣很难更新的问题。提出了基于搜索历史的用户兴趣的表达方法和自动隐式学习算法。全面地描述了用户兴趣模型的建立及通过自动隐式学习算法不断更新、优化模型的处理过程，并给出了对模型的评价标准。     

基于深度学习挖掘用户搜索主题研究

主要研究了基于深度学习技术挖掘用户搜索主题相关的感兴趣内容。通过深度挖掘算法分析用户搜索记录、查询历史以及用户感兴趣的相关文档视为用户搜索主题数据的来源,进而挖掘兴趣主题。挖掘模型主要采用向量空间模型,将用户搜索主题模型表示成用户搜索主题向量形式。形成主题和用户兴趣关系网,用户搜索主题向量的构造过程:选择一组用户查询词,并对它们进行深度挖掘分类,最后用它们构造用户搜索主题特征向量,进而分析用户兴趣点。结合用户随着时间的变化,以及过程中有不用的搜索词,以及无关的搜索噪声词去掉,调整兴趣度,用户搜索主题需要具有更新学习机制,动态跟踪了用户兴趣变化趋势。该用户搜索主题研究过程克服了数据稀疏、类别偏差、扩展性差等缺点。实验结果表明,该模型识别用户搜索主题准确率良好。     

基于知识图谱的多目标学习资源推荐算法

为解决在线学习中出现的“认知过载”和“学习迷航”等问题,针对用户的个性化学习需求,同时考虑知识点之间的逻辑关系,本文将知识图谱融入学习资源推荐模型.首先构建了学科知识图谱、学习资源模型和用户数学模型,综合考虑用户的兴趣偏好、用户知识库与学习资源所涵盖知识点的关联度以建立多目标优化模型.然后使用自适应多目标粒子群算法对模型求解,基于个体拥挤距离降序排列缩减外部种群规模,获得了分布特征良好的两目标Pareto前沿,输出推荐资源序列.实验时通过与标准多目标粒子群算法对比并使用HV、IGD指标对模型进行评价,验证了其多样性和稳定性,证明了算法良好的全局寻优和收敛性能.采用五折交叉验证了算法良好的推荐效用.     

一种基于长短期用户表示和多视角学习的新闻推荐方法

个性化新闻推荐系统可以帮助用户在海量新闻中快速获取感兴趣内容。用户的兴趣有长期和短期之分，新闻信息也分多种类别，而现有的方法往往基于单类别信息学习新闻的表示。基于此，提出一种融合长短期用户表示、多特征新闻表示的方法。采用基于协同注意力机制的多视角学习方法构建新闻编码器，从新闻的标题、分类和摘要特征中学习统一的新闻表示；利用改进的新闻表示在基于长短期兴趣的用户编码器中进一步细粒度学习用户表示。在真实新闻数据集上的实验结果表明，该方法与其他推荐算法相比在准确率上有明显提高。     

基于改进型协同过滤的网络学习资源推荐算法

面对网络学习资源的信息过载问题,如何根据用户的偏好推荐其感兴趣的学习资源是网络教育智能化的关键应用.协同过滤推荐算法无需构建资源的特征描述,经常应用于形式多样的网络学习资源推荐,但传统协同过滤推荐算法具有评分矩阵稀疏和冷启动问题.针对这两个问题,提出基于改进型协同过滤的网络学习资源个性化推荐算法.该算法首先将用户对资源...     

基于Web挖掘与相关反馈的多层次用户兴趣挖掘算法

针对现有用户兴趣挖掘算法单一的缺点，提出了基于Web挖掘与相关反馈的多层次用户兴趣挖掘算法，在充分挖掘Web内容的同时，又将用户对网页的相关性反馈引入到算法中，实现显式提交信息与自动隐式学习相结合。实验证明该算法能较好地描述用户的兴趣类型及兴趣度，为实现个性化信息检索奠定了基础。     

Design and evaluation of a gamified system for ERP training

Enterprise Resource Planning Systems (ERP) facilitate the flow of information within a company by storing data in common databases. These systems offer a holistic view of the organization because they reduce information redundancy, offer information in real time, help with process standardization, and improve information flow and communication among employees. Nevertheless, the benefits attributed to an ERP implementation can be lost without an effective user training. Previous studies have observed that common training mechanisms don't provide meaningful learning to users, and that user satisfaction rates range from neutral to low. Therefore, in this paper we study the effects that gamification has on ERP training concerning user learning and user satisfaction. Gamification is the use of game elements and game design techniques in non-gaming environments. Several applications prove that gamified systems increase user engagement and performance. Our hypotheses were that a gamified system for ERP training improve user learning and user satisfaction levels during the training period. To test our hypotheses, we designed and evaluated a gamified system. The results showed that users trained using a gamified system performed better than those trained using a conventional, non-gamified, training mechanism.     

Discriminative effect of user influence and user responsibility on information system development processes and project management

Context

User participation in information system (IS) development has received much research attention. However, prior empirical research regarding the effect of user participation on IS success is inconclusive. This might be because previous studies overlook the effect of the particular components of user participation and other possible mediating factors.

Objective

The objective of this study is to empirically examine how user influence and user responsibility affect IS project performance. We inspect whether user influence and user responsibility improve the quality of the IS development process and in turn leads to project success, or if they have a direct positive influence on project success.

Method

We conducted a survey of 151 IS project managers in order to understand the impact of user influence and user responsibility on IS project performance. Regression analysis was conducted to assess the relationship among user influence, user responsibility, organizational technology learning, project control, user-developer interaction, and IS project management performance.

Results

This study shows that user responsibility and user influence have a positive effect on project performance through the promotion of IS development processes as mediators, including organizational technology learning, project control, and user-IS interaction.

Conclusion

Our results suggest that user responsibility and user influence respectively play an important role in indirectly and directly impacting project management performance. Results of the analysis imply that organizations and project managers should use both user participation and user influence to improve processes performance, and in turn, increase project success.     

User Profile Modeling in the context of web-based learning management systems

Over the past two decades, great research efforts have been made towards the personalization of e-learning platforms. This feature increases remarkably the quality of the provided learning services, since the users’ special needs and capabilities are respected. The idea of predicting the users’ preferences and adapting the e-learning platform accordingly is the focal point of this paper. In particular, this paper starts with the main requirements of an advanced e-learning system, explains the way a user navigates in such a system, presents the architecture of a corresponding e-learning system and describes its main components. Research is focused on the User Model component, its role in the e-learning system and the parameters that comprise it. In this context, Bayesian Networks are used as a tool for the encoding, learning and reasoning of probabilistic relationships, with the aim to effectively predict user preferences. In support of this vision, four different scenarios are presented, in order to test the way Bayesian Networks apply in the e-learning field.     

自适应网络学习用户界面设计和实现

An adaptive user interface helps to improve the quality of human-computer interaction. Most people at present join to Web-Based Learning by common browser. Due to the one-fits-all user interface, they have to face with the problem of lack of the support on personalized learning. The design and implementation of the adaptive user interface for Web-based learning in this paper is grounded in our work done before, for example interaction model,adaptive user models including domain models. The adaptivity is mainly expressed on learning contents and representation including layout as well as operation.     

基于SVM增量学习的用户适应性研究

1.引言人机交互技术(Human-Computer Interaction,HCI)是一门新兴的边缘学科,在近十几年内迅速形成并得到发展。人机交互是对于人、计算机技术以及它们相互影响方式的研究,其目的是为了使计算机技术更加适合于人。用户意图预测(User Intention Prediction)是智能化人机交互的关键所在。对一个多用户系统而言,不同的用户具有不同的特性或习惯,     

基于中图分类法的科技文献过滤系统的研究与实现

提出了一种基于中图分类法的用户兴趣模型,形式化地描述了用户兴趣模型的建立和学习过程,并在此基础上开发了一种科技文献过滤系统.     

基于SVM用户建模的核函数选择研究

用户建模是从用户偏好数据中建立用户偏好模型的过程,用户偏好数据具有系统运行初期的稀疏性和非线形的特点。支持向量机(Support Vector Machine,简称SVM)具有小样本学习、非线形处理的能力,是合适的用户建模工具。SVM的非线形处理能力主要依赖于核函数,采用不同的核函数进行建模对模型的预测效果有重大影响。本文重点研究核函数的选择对基于SVM建模方法的影响,从中选取了表现较优的小波核函数,构建性能突出的SVM进行用户建模。实验证明该建模方法可以有效地从小样本数据中学习用户偏好信息,建立反映用户真实偏好的用户模型。     

基于粒计算的多粒度用户画像

现有的用户画像分析模型使用单一模型单一粒度的学习方式处理异构多源的原始数据,限制分析模型的性能,无法完整展示多层次、多角度的用户画像特征.针对该问题,基于粒计算思想,文中提出多粒度用户画像分析模型.首先,构建数据的多粒度表示结构,粒化原始数据.再根据数据粒度结构,提出基于集成学习的粒度提升算法,用于融合低粒层的数据信息以得到高粒层的数据表示.最后,在多个粒层数据表示上进行用户画像分析,展示一个较全面的用户画像.实验表明,相比单一粒度的用户画像,多粒度的用户画像更全面、立体和丰富     

用户信息保护下的学习资源知识点自动提取

提出一种用户信息保护下的网络学习资源知识点内容自动提取方法,即在信息保护层中加入用户信息保护状态HMM模型,一旦判断保护状态无效,自动退出知识点内容提取流程,防止用户信息受到侵犯。使用用户信息保护HMM模型,对4家学习网站227名用户的查询浏览行为、用户链接、用户配置信息的真实数据集进行了实验,结果表明,进行500次随机消息测试时,模型对用户信息保护状态的判断正确率为94%,对虚假安全消息的误判率为0. 04。根据4家学习网站在12。天中的用户评分数据,系统使用后的平均分数较系统使用前平均增幅达23.23%。     

Modeling user multiple interests by an improved GCS approach

User interest profile is the crucial component of most personalized recommender systems. The diversity and time-dependent evolving nature of user interests are creating difficulties in constructing and maintaining a sound user profile. This paper presents a simple but effective model, by using improved growing cell structures (GCS), to address this problem. The GCS is a kind of self-organizing map neural network with changeable network structure. By virtue of the clustering and structure adaptation capability of GCS, the proposed model maps the problem of learning and keeping track of user interests into a clustering and cluster-maintaining problem. Each cluster found by GCS represents an interest category of a user and the cluster maintaining, including cluster addition and deletion, corresponds to the addition of user's new interests and the removal of user's old interests. The proposed model has been validated by a set of experiments performed on a benchmark dataset. Results from experiments show that our model provides reasonable performance and high adaptability for learning user multiple interests and their changes.