基于微博用户兴趣话题的相似用户挖掘

相似用户挖掘是提高社交网络服务质量的重要途径，在面向大数据的社交网络时代，准确的相似用户挖掘对于用户和互联网企业等都有重要的意义，而根据用户自己的兴趣话题挖掘的相似用户更符合相似用户的要求。提出了一种基于用户兴趣话题进行相似用户挖掘的方法。该方法首先使用TextRank话题提取方法对用户进行兴趣话题提取，再对用户发表内容进行训练，计算出所有词之间的相似度。提出CP（Corresponding Position similarity）、CPW（Corresponding Position Weighted similarity）、AP（All Position similarity）、APW（All Position Weighted similarity）四种用户兴趣话题词相似度计算方法，通过用户和相似用户间关注、粉丝重合率验证相似用户挖掘效果，APW similarity的相似用户的关注/粉丝重合百分比为1.687%，优于提出的其他三种算法，分别提高了26.3%、2.8%、12.4%，并且比传统的文本相似度方法Jaccard相似度、编辑距离算法、余弦相似度分别提高了20.4%、21.2%、45.0%。因此APW方法可以更加有效地挖掘出用户的相似用户。     

Collective institutional work: the case of Airbnb in Amsterdam,London and New York

ABSTRACT

Given that online platforms disrupt established industries and challenge existing institutions, they can only be successful if their innovation becomes both legal and legitimate. This requires ‘institutional work’ that changes perceptions and regulations within society. Rather than only focussing on the online platform as the sole agent engaging in institutional work, our study analyses institutional work as a collective process. We investigate the case of home-sharing platform Airbnb and the process of institutional change its introduction prompted regarding short-term rental in Amsterdam, London and New York. We find, contrary to the popular view of online platforms as disruptive entrepreneurs, that the platform mainly focusses on creating new institutions rather than disrupting existing ones, and that users and non-users undertake most of the institutional work activities. We also show that different types of actors carry out different types of institutional work suggesting that the process of institutional work is highly distributed.     

为了满足使用者需求而设计——上海地铁研究·使用者需求

在上海地铁研究中,我们发现了许多使用者的不满。这些不满来自于设计师对使用者的缺乏了解和缺乏关注。通过对上海地铁真实的使用者、真实的地点和真实的时间的调研,我们分析出25项上海地铁使用者的需求。并提出希望设计师能够从这些使用者的需求出发,去作为使用者带来良好体验的设计。     

Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems

Recommender systems apply data mining and machine learning techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep users problem responsible for the increased error rate in collaborative filtering based recommender systems. This paper makes the following contributions: we show that (1) the presence of gray-sheep users can affect the performance – accuracy and coverage – of the collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of community can be found empirically. We propose various improved centroid selection approaches and distance measures for the K-means clustering algorithm; (3) content-based profile of gray-sheep users can be used for making accurate recommendations. We offer a hybrid recommendation algorithm to make reliable recommendations for gray-sheep users. To the best of our knowledge, this is the first attempt to propose a formal solution for gray-sheep users problem. By extensive experimental results on two different datasets (MovieLens and community of movie fans in the FilmTrust website), we showed that the proposed approach reduces the recommendation error rate for the gray-sheep users while maintaining reasonable computational performance.     

基于局部重要性采样的SAR图像纹理特征提取方法

合成孔径雷达（Synthetic aperture radar,SAR）图像因为相干斑现象和目标响应的空间变化呈现出一种纹理特性,局部二进编码等局部图像特征 在光学纹理描述中获得较好的结果,但光学纹理特征在描述SAR图像纹理特性中因为相干成像特性往往失效. 本文 在前期工作纹理特征框架的基础上,提出了一种局部重要性采样二进编码的SAR图像纹理特征（Feature extraction based on local important sampling binary,LISBF）描述方法:首先,利用样本图像对局部采样位 置进行随机自适应采样,基于重要性采样（Important sample,IS）方法输出递归学习位置结果;然后,利用学习出的纹理重要采样点对进 行二进特征编码;最后,通过映射和统计生成描述算子. 该特征较固定位置采样能够获取更大范围信息,同时能通 过采样避免特征维数的急剧增大;通过自适应学习重要性关键点较随机采样 更容易捕捉纹理固有信息;较好地适应了SAR图像极低信噪比和斑点现象的纹理. 本文将该特征用于真实图像和标准纹理库的分类研究,实验结果证明了该特征的有效性.     

Adaptive training interfaces for less-experienced,elderly users of electronic devices

A great number of complex electronic devices are now part of our everyday lives. While many of us learn to handle these products by trial and error; others, especially older users with little experience in using electronic devices, need support. In order to allow the user maximum flexibility in terms of learning time and location, a training programme is presented which is implemented as part of the software embedded in the product itself. Particular focus is placed on the effect of adaptive training on learning. In this study, the training versions differed in their ability to adjust their complexity to the user's experience (adaptive user interface complexity) and their capability to support the learner by prompting them during the learning process (adaptive training advice). The results show that the adjustment of complexity had a positive effect on users’ experience: elderly users who trained with an adaptive interface were more successful in learning to use a mobile phone. Adaptive training advice, however, was found to have no significant effects on learners’ success and reduced their self-efficacy. This work offers guidelines on how to design integrated training applications for electronic devices that successfully help elderly users with little prior experience.     

基于重要边缘点的时间序列异常模式检测算法

在分析边缘算子的思想和现有时间序列模式表示方法基础上,将边缘点方法和重要点方法相结合,提出了基于重要边缘点的时间序列模式表示算法。算法按各观测点的边缘化程度,提取重要的边缘点将时间序列分成多个子线段,通过分析直线段之间的相似性,发现异常的序列模式。从理论和实验两方面对算法进行了分析和验证,结果表明,算法复杂度较低,模式表示误差小,能够满足大规模时间序列数据模式表示的要求。     

基于改进SVM的互联网用户分类

由于传统模型大量约束样本,导致其学习能力下降,因此设计一个基于改进支持向量机(Support Vector Machine,SVM)的互联网用户分类模型.该模型通过构造样本数据,模拟互联网用户的浏览轨迹;根据用户偏好,制定全新的用户分类策略;基于改进支持向量机,实现对互联网用户的分类.性能测试:3次实验下,此次设计的模型分类准确率平均值为98.56%,超出了预设的期望值,具备分类能力.对比测试:与两组传统用户分类模型相比,此次设计的模型,面对不断增加的样本数据,同样能保持高水平的学习能力.     

Probability-based location anonymity algorithm

As one of the most effective location privacy preservation technologies, the k-anonymity model provides safeguards for location privacy of the mobile client against vulnerabilities for abuse by constructing an anonymous area of k users including the protected one. However, most existing k-anonymity models only utilize the users who are sending requests at recent time. If there are not enough requesting users, the generated anonymous area of the k-anonymity model will be larger than expected. In this paper, a Probability-based Location Anonymity (PLA) algorithm is proposed for protecting location privacy of the mobile users in a road network. The PLA model takes advantage of the historical path track of the users who are not sending the request currently, and then computes the probability into the anonymous section so that it can greatly reduce the size of the anonymous area. Experimental results show that the PLA algorithm is superior to the k-anonymity and it increases its anonymous efficiency enormously.     

非时隙结构认知无线网络中非授权用户传输时延分析

在非时隙结构认知无线网络中,以随机侦听多址方案为基础构建了系统的数学模型,利用柯尔莫哥洛夫费勒前向方程得出系统的稳态概率,并采用拉普拉斯斯帝尔阶斯变换推导出非授权用户数据包的平均传输时延.数值分析表明,信道忙的概率和平均退避时间与传输时延呈正比关系,不同的载波侦听多址方案对传输时延影响很大,其中全侦听多址方案的传输时延最小.