社交网络中上下文感知协同过滤算法

围绕上下文感知推荐技术和社会化网络推荐技术的局限性展开研究,提出一种基于社会化网络环境下的名为HCCF的上下文感知协同过滤方法。在充分考虑上下文感知推荐系统实际问题的基础上,首先量化了不同维度的上下文对推荐系统所产生的影响,并在此基础上定义了上下文影响系数。在此基础上引入了社会化网络环境中不同用户之间的相互影响,并采用社会化网络用户信任度进行衡量,最后对上下文因素和社会化网络用户信任度进行综合考虑,提出一种新的相似度计算方法。理论分析和在真实数据集上的实验结果表明,相对于单纯基于上下文的系统过滤算法以及社会化网络推荐方法而言,该算法的准确性和推荐效率均得到一定程度的提升。     

基于层次隐马尔可夫模型和神经网络的个性化推荐算法

传统推荐系统将推荐准确性作为主要目标,而推荐结果的多样性和个性化有所欠缺.对此,设计一种基于层次隐马尔可夫模型和神经网络的推荐算法.采用层次隐马尔可夫模型建模用户喜好和上下文环境的关系,并通过隐马尔可夫模型预测上下文.设计神经网络结构来解决协同过滤推荐的问题,同时神经网络满足贝叶斯个性化排序的条件,实现对推荐列表的个性...     

基于贝叶斯网络的多Agent服务推荐机制研究

普适计算环境中的服务推荐需要满足系统异构性和移动性的要求。提出了一种基于贝叶斯网络的多Agent服务推荐机制并进行实现,将贝叶斯网络和聚类方法应用于服务推荐中,并设计了推荐模型自学习机制,充分考虑了上下文对服务推荐的影响及改进。实现系统由完成历史上下文汇集、知识训练、决策推荐和自学习功能的多个Agent构成,通过Agent之间的通信内容设计,在Agent之间建立流程控制和数据共享通道。     

基于上下文的餐饮推荐算法

互联网的发展,为餐饮用户的上下文信息获取提供了基础。在用户选择适合其餐饮模式的前提下,加入动态上下文因素以满足用户的需求。为改善传统的协同过滤方法应用于餐饮O2O推荐存在的稀疏矩阵、冷启动等问题,设计了基于上下文后过滤的协同过滤推荐方法。先通过转化基于项目属性效用的评分矩阵,计算出用户对项目评分的偏好相似度。根据用户的评分偏好和静态上下文信息构建相似组,结合上下文信息加权的贝叶斯模型,采用基于KL散度的加权方法进行动态偏好分析,解决上下文信息缺乏时难以构建概率模型以及推荐系统的用户冷启动问题。实验结果显示,随着邻居数目增加时,基于上下文的推荐算法与传统的协同过滤算法相比,能维持较高的准确率和召回率,验证了推荐算法的有效性。     

基于Bayesian Network的学习资源库推荐系统构建与实现

针对学习资源使用者的特点和当前网络学习模型的不足,提出运用贝叶斯网络建立一种个性化学习者模型。基于用户决策方案指导资源库的建设,提出了一种新的学习资源推荐算法,使学习资源的呈现符合学习者认知发展水平和个性特征,改善资源库的组织结构,实现智能化、个性化的学习资源库推荐系统。实践证明,对于本系统所推荐的学习资源,学习者非常满意。     

一种基于协同上下文关系学习的同城活动推荐算法

新兴的基于活动的社交网络以活动为核心,结合线上关系与线下活动促进用户真实、有效的社交关系的形成,但过多的活动信息会使用户难以分辨和选择.结合上下文进行个性化同城活动推荐,是解决活动信息过载问题的一种有效手段.然而大部分现有的同城活动推荐算法都是从用户参与活动记录中间接统计用户对上下文信息的偏好,忽略了两者之间潜在的交叉影响关系,从而影响了推荐结果的有效性.为了解决用户参与活动偏好与上下文信息潜在交叉影响关系利用不足的问题,提出了一种基于协同上下文关系学习的同城活动推荐算法(colletive contextual relation learning,简称CCRL).首先,对用户参与活动记录和活动主办方、活动内容、活动地点、举办时间等相关上下文信息进行关系建模;然后,采用多关系贝叶斯个性化排序学习方法进行协同上下文关系学习及同城活动推荐. Meetup数据集上的实验结果表明,该算法在多项指标上均优于现有的主流活动推荐算法.     

图神经网络会话推荐系统综述

会话推荐立足于目标用户的当前会话,根据项目类别、跨会话的上下文信息、多种用户行为等辅助信息学习项目间的依赖关系,从而捕捉用户的长短期偏好进行个性化推荐。近年来,流行的深度学习系列方法已经成为会话型推荐系统这个研究热点的前沿方法,尤其是图神经网络的引入,使会话推荐系统的性能得到了进一步提升。鉴于此,该综述从问题定义与会话推荐因素出发,从构图方面进行分析;将相关工作分为基于图卷积网络、门控图神经网络、图注意力网络和其他图神经网络架构的会话推荐系统,并进行归纳与对比;对各工作实验部分中的损失函数类别、所选用的数据集和模型性能评估指标三方面进行深入分析。重点从算法原理和性能分析两方面对各模型框架进行评估和梳理,旨在对近五年基于图神经网络的会话推荐系统相关工作进行评述、总结与展望。     

个性化微博推荐算法

微博不同于传统的社会网络和电子商务网站,存在用户活跃程度低,微博数据稀疏和用户兴趣动态变化等特点,将传统推荐算法应用于微博推荐时,效果并不理想。提出了一种基于贝叶斯个性化排序的微博推荐算法,对用户进行个性化微博推荐。该基于贝叶斯个性化排序的微博推荐算法,以微博对的形式提取微博系统中的隐式信息,对这些微博对进行学习,从而得到用户对不同微博的兴趣值。根据每条微博发出的时间,估计每条微博对的可信度。发出时间越接近的微博对,它的可信度就越高,并且对用户的兴趣值影响就越大。在新浪微博的真实数据上进行实验和评测,结果表明该基于贝叶斯个性化排序的微博推荐算法相比于对比算法,在进行微博推荐时有更好的效果。     

融合神经网络和泊松分解的兴趣点推荐算法

针对兴趣点推荐系统存在的隐式反馈建模用户-POI交互准确率不高和忽视用户签到数据的隐性反馈属性的问题。提出了一种新颖的兴趣点推荐算法。具体而言,采用一种基于神经网络的排序算法来捕获用户-兴趣点的交互关系,结合泊松分解算法和贝叶斯个性化排序技术建模用户的签到行为,将上述2个步骤得到的算法整合到统一的推荐算法架构中,从而提供兴趣点推荐服务。实验结果表明,提出的算法推荐性能优于传统主流先进兴趣点推荐算法。     

面向个性化推荐的两层混合图模型

小世界网络在聚类应用中具有良好的性质,贝叶斯网络在概率推理中也得到了广泛的研究.将小世界网络和贝叶斯网络结合起来,形成一种混合图模型,并将该模型用于个性化推荐系统中.该混合图模型由两层组成,分别是用户层和商品层.其中小世界网络用于描述用户层内用户-用户结点间的关系,贝叶斯网络用于描述商品层内商品-商品结点以及层间用户-商品结点间的偏好关系.对小世界网络的用户聚类方法、贝叶斯网络结构和参数学习方法以及两层图模型的推荐算法进行描述,实验分析表明,该模型能够很好地表示用户-用户、商品-商品以及用户-商品间的关系,推荐结果具有良好的准确度.     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.