基于差分隐私和SVD++的协同过滤算法

协同过滤技术在推荐系统的实现中具有广泛的应用,协同过滤以用户对商品项目的评价分数为依据,而这些评价有可能反映用户某些不欲为人知的喜好特点,因此,对具备隐私保护能力的协同过滤模型的研究引起了普遍的关注.SVD++是当前最为常用的协同过滤模型之一,差分隐私模型则是近十年来隐私保护理论最重要的研究进展之一,将两者相结合提出3种基于差分隐私和SVD++的协同过滤模型:基于梯度扰动的SVD++隐私保护模型、基于目标函数扰动的SVD++隐私保护模型和基于输出结果扰动的隐私保护模型.理论分析和实验结果显示,所提出的算法不仅能为用户的隐私安全提供可靠的保障,而且还可保持较高的预测准确度.     

面向推荐应用的差分隐私方案综述

大数据时代背景下,各行各业希望能基于用户行为数据来训练推荐模型,为用户提供精准推荐,所用数据的共性特点为总量庞大、携带敏感信息、易于获取.推荐系统在带来精准推荐和市场盈利的同时也正在实时分享着用户的隐私数据,差分隐私保护技术作为一门隐私保护技术,能够巧妙地解决推荐应用中存在的隐私泄露问题,其优势在于不需要考虑攻击方所具备的任何相关的背景知识、严格地对隐私保护进行了定义、提供了量化评估方法来保证数据集(在不同参数条件下)所提供的隐私保护水平具有可比较性.首先简述了差分隐私的概念和主流推荐算法的近期研究成果,其次重点分析了差分隐私与推荐算法相结合的应用情况,涉及的推荐算法有矩阵分解、深度学习推荐、协同过滤等,并对基于差分隐私保护的推荐算法的准确性进行了对比实验;然后讨论了与每种推荐算法结合的使用场景以及目前仍存在的问题,最后对基于差分隐私的推荐算法的未来发展方向提出了有效建议.     

HRS-DC：基于深度学习的混合推荐模型

针对传统的矩阵分解算法,仅利用评分信息作为推荐依据,当评分数据稀疏时,不能准确获取隐式反馈,影响推荐的准确性,充分利用辅助信息进行隐式特征的提取成为研究热点之一,提出一种基于深度学习的推荐模型HRS-DC,利用深度神经网络和卷积神经网络从辅助信息中分别提取出用户和项目的隐性特征向量,再将特征向量经过改进的神经协同过滤得出新的评分矩阵。通过在三个真实的数据集上进行验证,与概率矩阵分解（PMF）、协同过滤主题回归（CTR）、协同过滤深度学习（CDL）、卷积矩阵分解ConvMF算法相比提高了评分预测的准确性,也在一定程度上缓解了冷启动问题。     

基于非负矩阵分解的隐私保护协同过滤算法

提出一种基于非负矩阵分解的隐私保护协同过滤推荐算法．该算法在用户数据收集过程中采用随机扰动技术,并使用非负矩阵分解对数据进行处理,从而形成隐私保护功能,并在此基础上产生推荐．理论分析和实验结果表明,该算法在保护用户个人隐私的基础上,能够产生具有一定精确性的推荐结果.     

三通道神经协同过滤算法

传统的神经协同过滤算法在隐式反馈数据集上对用户和项目建模,由于隐式反馈数据天然带有很强的噪音,这给模型的学习带来了挑战.为了缓解该问题,文中提出了一种基于三通道的神经协同过滤算法,该方法使用自编码器去挖掘用户和项目的特征向量,然后结合用户和项目的辅助信息一起通过多层感知机去学习特征向量不同维度之间的高阶交互关系,并将其与传统的神经协同过滤算法融合,以此来提高模型的泛化能力和命中率.此外,在隐式反馈数据集上进行负采样不易且采样结果会极大程度影响模型的表现,文中采用一种基于传统矩阵分解的概率负采样方法克服这个问题,提高了模型的鲁棒性.本文在公开数据集MovieLens上进行了大量实验,实验结果表明基于本文提出的算法比其他先进算法有更优的表现.     

基于混合相似度和差分隐私的协同过滤推荐算法

现有协同过滤推荐算法中,存在的评分片面、主观性强、评分矩阵稀疏性等因素影响了推荐的精确度,并在推荐中存在隐私泄露等问题.针对上述问题,提出一种基于混合相似度和差分隐私的协同过滤推荐算法.该算法基于多种相似度进行加权计算构造混合相似度,提高推荐精度;以混合相似度作为质心更新和分类条件,利用改进的K-means算法将与目标用户相似度高的用户进行聚类;采用枚举方法在目标用户集中划分子集,并基于混合相似度构建效用函数,利用差分隐私指数机制在各子集中选择邻居集合,保护用户隐私;最后在邻居集合中选择出评分值最高的项目进行推荐.实验结果表明,该算法在保护用户隐私的同时,有效提高了推荐的精确度.     

基于LDA的隐式标签协同过滤推荐算法

固定标签协同过滤推荐算法,未充分考虑标签因子的多样化,主要依靠人工标记,扩展性不强,主观因素多。本文从用户的喜好特征因素角度出发,在固定标签协同过滤推荐算法的基础上,提出一种隐式标签协同过滤推荐算法。该算法利用LDA主题模型生成项目文本的隐式标签,得到项目-标签特征权重,根据算法性能优化的要求选择标签数量,将项目-标签矩阵与用户评分矩阵结合得到用户对标签的偏好矩阵,最后通过协同过滤算法产生推荐。实验结果表明,本文提出的基于LDA的隐式标签协同过滤推荐算法缓解了数据稀疏性问题,项目推荐的召回率、准确度和F1值有较大提升。     

融合显/隐式反馈的社会化协同排序推荐算法

传统的基于评分预测的社会化协同过滤推荐算法存在预测值与真实排序不匹配的固有缺陷,而基于排序预测的社会化协同排序推荐算法更符合真实的应用场景。然而,现有的大多数基于排序预测的社会化协同排序推荐算法要么仅仅关注显式反馈数据,要么仅仅关注隐式反馈数据,没有充分挖掘这些数据的价值。为充分挖掘用户的社交网络和推荐对象的显/隐式评分信息,同时克服基于评分预测的社会化协同过滤推荐算法存在的固有缺陷,在xCLiMF模型和TrustSVD模型基础上,提出一种新的融合显/隐式反馈的社会化协同排序推荐算法SPR_SVD++。该算法同时挖掘用户评分矩阵和社交网络矩阵中的显/隐式信息,并优化排序学习的评价指标预期倒数排名（ERR）。在真实数据集上的实验结果表明,采用归一化折损累计增益（NDCG）和ERR作为评价指标,SPR_SVD++算法均优于最新的TrustSVD、MERR_SVD++和SVD++算法。可见SPR_SVD++算法性能好、可扩展性强,在互联网信息推荐领域有很好的应用前景。     

差分隐私保护在推荐系统中的应用研究

推荐系统已经成为Internet商家给用户提供个性化服务的高级商务智能平台之一。然而,用于研究推荐系统的数据信息里往往存在能够被攻击者直接或者间接获取的个人隐私。近年来受到极大关注的差分隐私保护是一种非常严格的、可证明的隐私保护模型。针对目前流行的协同过滤算法之一的矩阵分解进行了研究,提出了采用差分隐私保护技术对原始输入数据进行预处理和扰动处理的新方法。最后通过在真实数据集上进行相关实验验证,结果表明文中提出的带差分隐私保护的矩阵分解算法达到了预期：一方面既能保护用于做推荐研究的原始数据集的隐私,另一方面又没有严重影响推荐的准确率。     

推荐系统中的隐私保护矩阵分解算法研究

针对协同过滤推荐系统中的用户数据和模型保护问题,提出一种隐私保护矩阵分解算法.该算法基于分布式架构,其梯度下降优化过程由服务器和各个用户相互协作完成.每轮迭代中,服务器仅从客户端得到物品隐藏因子向量梯度更新信息,从而有效保护了用户评分和推荐模型.基于多方安全求和的原理,在梯度更新过程中加入混淆机制,实现了对用户评分"存在性"的保护.开发系统原型并与现有方法进行实验对比,结果表明,该方法在保护用户隐私的同时能够提供更好的推荐准确度.     

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。     

煤矿井下安全监控分站的设计及其仿真实现

为了设计一种具有低成本、低功耗、易操作、功能强且可靠性高的煤矿井下安全分站,针对煤矿安全生产实际,文章提出了采用MCS-51系列单片机为核心、具有CAN总线通信接口的煤矿井下安全监控分站的设计方案;首先给出煤矿井下安全监控分站的整体构架设计,然后着重阐述模拟量输入信号处理系统的设计过程,最后说明单片机最小系统及其键盘、显示、报警、通信等各个组成部分的设计;为验证设计方案的可行性与有效性,使用Proteus软件对设计内容进行仿真验证,设计的煤矿井下安全监控分站具有瓦斯、温度等模拟量参数超标报警功能和电机开停、风门开闭等开关量指示功能;仿真结果表明:设计的煤矿井下安全监控分站具有一定的实际应用价值.     

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     

View-based model-driven architecture for enhancing maintainability of data access services

In modern service-oriented architectures, database access is done by a special type of services, the so-called data access services (DAS). Though, particularly in data-intensive applications, using and developing DAS are very common today, the link between the DAS and their implementation, e.g. a layer of data access objects (DAOs) encapsulating the database queries, still is not sufficiently elaborated, yet. As a result, as the number of DAS grows, finding the desired DAS for reuse and/or associated documentation can become an impossible task. In this paper we focus on bridging this gap between the DAS and their implementation by presenting a view-based, model-driven data access architecture (VMDA) managing models of the DAS, DAOs and database queries in a queryable manner. Our models support tailored views of different stakeholders and are scalable with all types of DAS implementations. In this paper we show that our view-based and model driven architecture approach can enhance software development productivity and maintainability by improving DAS documentation. Moreover, our VMDA opens a wide range of applications such as evaluating DAS usage for DAS performance optimization. Furthermore, we provide tool support and illustrate the applicability of our VMDA in a large-scale case study. Finally, we quantitatively prove that our approach performs with acceptable response times.     

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.     

A pragmatic multi-method investigation of discrepant technological events: Coping,attributions, and ‘accidental’ learning

Discrepant technological events or situations that entail a problem, a misunderstanding or a difficulty with the Information Technology (IT) being employed, are common in the workplace, and can lead to frustration and avoidance behaviors. Little is known, however, about how individuals cope with these events. This paper examines these events by using a multi-method pragmatic approach informed by coping theory. The results of two studies – a critical incident study and an experiment – serve to build and test, respectively, a theoretical model that posits that individuals use a variety of strategies when dealing with these events: they experience negative emotions, make external attributions, and adopt engagement coping strategies directed at solving the event, eventually switching to a disengagement coping strategy when they feel they have no control over the situation. Furthermore, users’ efforts may result in ‘accidental’ learning as they try to overcome the discrepant IT events through engagement coping. The paper ends with a discussion of the results in light of existing literature, future opportunities for research, and implications for practice.