Online Consumers’ Responses to Deal Popularity as an Extrinsic Cue

Deal popularity indicates the popularity of a product or service within a specific deal duration and is being used by many e-tailers in their websites/apps. While deal popularity is receiving increasing attention by academics and practitioners alike, its effect as an extrinsic cue remains under-researched. Using an experimental design, this study investigates the effect of deal popularity on expected service quality in an e-tailing environment. It is found that deal popularity influences service quality expectations for credence services, but not experience services. High deal popularity positively influences consumers’ intention to purchase and willingness to recommend credence services. E-tailers of services need to pay attention to deal popularity displayed for credence services and leverage on the positive effect it has on consumers.     

基于因果模型的主题热度计算与预测方法

网络是目前最重要的信息传播渠道,其自由性和丰富性使得信息迅速传播。挖掘网络中的热点主题对政府政策的制定、企业经营决策的调整可以提供强有力的支持,并能够满足网民对热点主题的关注需求。主题数量的庞大使得主题热度值的计算尤为重要,该文分析热度的形成原因,基于因果模型并采用面板数据,给出一种较为客观可行的主题热度计算模型。该模型使用易于获取的数据进行计算,给出较为客观的热度度量,进而便于不同主题、不同日期间的热度对比。在此基础上,通过对热度变化规律的考察,提出一种基于多峰高斯曲线拟合热度变化进行主题热度预测的思路。     

内容分发网络中内容流行度集中性的研究

内容流行度分布的集中程度是决定内容分发网络处理性能的关键因素,定量衡量内容流行度的集中程度可以为内容分发网络相关研究、设计和仿真等工作提供重要依据。在以往相关研究工作基础上给出了内容流行度的规范定义,提出了内容流行集中度及百分点集中度的定义,并借鉴经济学中定量描述财富分布不均等程度相关方法给出了定量描述内容流行度分布集中性的系列方法,包括Lorenz曲线、Gini系数、差异系数和Theil系数。仿真结果表明:给出的内容流行度和内容流行集中度的定义以及针对内容流行度集中性的相关定量描述可以准确描述信息通信系统中内容流行度及其集中性分布特征,可以作为研究内容分发网络的理论依据之一。     

基于多级注意力机制网络的app流行度预测

移动app流行度预测从运营策略优化到短期广告投资都具有重要意义.利用应用市场提供的丰富数据,挖掘不同特征指标与流行度之间的动态关联,从而预测未来一段时间内app的流行度变化过程及其原因,对于开发者、投资商和应用市场三方都具有应用价值.然而,app流行度高度动态变化,其影响因素十分复杂,包括app自身迭代演化、用户反馈、同类产品的市场竞争等.目前,针对app流行度建模与预测的研究工作相对较少,大多通过构造人工特征并构建与流行度的关联来进行预测,在模型的计算性能、预测精度和结果可解释性等方面存在可提升的空间.因此针对app流行度建模与预测提出一种基于注意力机制的深度神经网络模型DeePOP,并针对复杂影响因素进行分级建模.通过时间级自序列模块捕获对历史流行度的长期依赖,利用局部、全局特征层级模块构建影响特征与流行度的非线性关系.同时,注意力机制为不同模块提供自适应能力,以捕获与流行度变化最相关的历史状态并针对预测结果提供一定的解释.实验结果表明:与现有流行度预测方法相比,DeePOP能够快速高效地进行app流行度建模与预测,预测均方根误差为0.089.     

一种细粒度均衡的新型分布式文件放置算法

为保证访问负载的均衡分布,分布式存储系统往往依赖访问热度信息进行文件放置。然而,访问热度信息在文件存入系统时刻并不可知,并且随时间不断变化,依赖访问热度信息的放置算法需要不断调整文件的存储位置,产生高昂的迁移成本。本文提出一种细粒度均衡的新型分布式文件放置算法。该算法利用文件访问热度同已创建时间之间的相关性,通过保证各节点所存储数据量在创建时间维度上的细粒度相似性,实现较好的访问负载均衡。该算法仅基于文件的创建时间属性,该属性在文件存入系统时刻属于已知信息并且不随时间变化。实验结果表明,相较于HDFS系统的随机放置算法,本文算法能够更好地实现访问负载的均衡分布,提高访问性能。     

Web search engine: Characteristics of user behaviors and their implication

In this paper, first studied are the distribution characteristics of user behaviors based on log data from a massive web search engine. Analysis shows that stochastic distribution of user queries accords with the characteristics of power-law function and exhibits strong similarity, and the user' s queries and clicked URLs present dramatic locality, which implies that query cache and 'hot click' cache can be employed to improve system performance. Then three typical cache replacement policies are compared, including LRU, FIFO, and LFU with attenuation. In addition, the distribution character-istics of web information are also analyzed, which demonstrates that the link popularity and replica pop-ularity of a URL have positive influence on its importance. Finally, variance between the link popularity and user popularity, and variance between replica popularity and user popularity are analyzed, which give us some important insight that helps us improve the ranking algorithms in a search engine.     

Towards a predictive cache replacement strategy for multimedia content

In recent years, telecom operators have been moving away from traditional broadcast-driven television, towards IP-based interactive and on-demand multimedia services. Consequently, multicast is no longer sufficient to limit the amount of generated traffic in the network. In order to prevent an explosive growth in traffic, caches can be strategically placed throughout the content delivery infrastructure. As the size of caches is usually limited to only a small fraction of the total size of all content items, it is important to accurately predict future content popularity. Traditional caching strategies only take into account the past when deciding what content to cache. Recently, a trend towards novel strategies that actually try to predict future content popularity has arisen. In this paper, we ascertain the viability of using popularity prediction in realistic multimedia content caching scenarios. The proposed generic popularity prediction algorithm is capable of predicting future content popularity, independent of specific content and service characteristics. Additionally, a novel cache replacement strategy, which employs the popularity prediction algorithm when making its decisions, is introduced. A detailed evaluation, based on simulation results using trace files from an actual deployed Video on Demand service, was performed. The evaluation results are used to determine the merits of popularity-based caching compared to traditional strategies. Additionally, the synergy between several parameters, such as cache size and prediction window, is investigated. Results show that the proposed prediction-based caching strategy has the potential to significantly outperform state-of-the-art traditional strategies. Specifically, the evaluated Video on Demand scenario showed a performance increase of up to 20% in terms of cache hit rate.     

防震减灾知识普及率抽样调查系统的设计与实现

介绍了防震减灾知识普及率抽样调查的数学模型和软件系统的设计方法,运用Java高级程序设计语言,采用struts2＋Hibernate＋spring框架,结合DWR＋ExtJs技术实现了在线调查市县防震减灾知识普及率的软件系统,并应用到红河州石屏县等3个县市5类人群的防震减灾知识普及率的抽样调查中。     

Weblog success: Exploring the role of technology

Weblogs have recently gained considerable media attention. Leading weblog sites are already attracting millions of visitors. Yet, success in the highly competitive world of weblogs is not easily achieved. This study seeks to explore weblog success from a technology perspective, i.e. from the impact of weblog-building technology (or blogging tool). Based on an examination of 126 highly successful weblogs tracked over a period of 3 months, we categorized weblogs in terms of popularity rank and growth, and evaluated the relationship between weblog success (in terms of popularity) and technology use. Our analysis indicates that weblog success is associated with the type of blogging tool used. We argue that technology characteristics affect the presentation and organization of weblog content, as well as the social interaction between bloggers, and in turn, affect weblog success or popularity improvement. Based on this analysis, we propose a techno-social success model for weblogs. This model postulates that a weblog's success is mainly associated with its ability to provide value for its users and readers at the content, the technology, and the social levels.     

Using Local Popularity of Web Resources for Geo-Ranking of Search Engine Results

Search engines retrieve and rank Web pages which are not only relevant to a query but also important or popular for the users. This popularity has been studied by analysis of the links between Web resources. Link-based page ranking models such as PageRank and HITS assign a global weight to each page regardless of its location. This popularity measurement has shown successful on general search engines. However unlike general search engines, location-based search engines should retrieve and rank higher the pages which are more popular locally. The best results for a location-based query are those which are not only relevant to the topic but also popular with or cited by local users. Current ranking models are often less effective for these queries since they are unable to estimate the local popularity. We offer a model for calculating the local popularity of Web resources using back link locations. Our model automatically assigns correct locations to the links and content and uses them to calculate new geo-rank scores for each page. The experiments show more accurate geo-ranking of search engine results when this model is used for processing location-based queries.     

Does popularity decide rankings or do rankings decide popularity? An investigation of ranking mechanism design

We analyze the music charts of an online digital music distributor that displays real time and weekly rankings on its website, and study how ranking policy should be set to maximize the value of its online music ranking service. The existing mechanism considers only streaming and download volumes, while the new ranking mechanism reflects more accurate preferences for popularity, pricing policy, and the slot effect based on the exponential decay of attention. The new ranking model is designed to verify correlations with two kinds of service volumes for popularity, pricing policy, and the slot effect. Slot mechanism design is analyzed in an heuristic way. Our analysis shows that music content sellers maximize benefits by assigning their own music items to the highest-ranking slot, which provides visibility. Also sellers can strategically design the slot size to influence the popularity of music items. Music content buyers gain indirect benefits by getting segmented ranking slots and reducing search costs. Empirical analysis illustrates the features of the online music industry and validates hypotheses constructed around the new ranking model. The results show that the new ranking mechanism is more effective.     

Assessing the authenticity of subjective information in the blockchain: a survey and open issues

World Wide Web - Blockchain, with its ever-increasing maturity and popularity, is being used in many different applied computing domains. To document the advancements made, researchers have...     

Computing and visualizing popular places

Data analysis and knowledge discovery in trajectory databases is an emerging field with a growing number of applications such as managing traffic, planning tourism infrastructures, analyzing professional sport matches or better understanding wildlife. A well-known collection of patterns which can occur for a subset of trajectories of moving objects exists. In this paper, we study the popular places pattern, that is, locations that are visited by many moving objects. We consider two criteria, strong and weak, to establish either the exact number of times that an object has visited a place during its complete trajectory or whether it has visited the place, or not. To solve the problem of reporting popular places, we introduce the popularity map. The popularity of a point is a measure of how many times the moving objects of a set have visited that point. The popularity map is the subdivision, into regions, of a plane where all the points have the same popularity. We propose different algorithms to efficiently compute and visualize popular places, the so-called popular regions and their schematization, by taking advantage of the parallel computing capabilities of the graphics processing units. Finally, we provide and discuss the experimental results obtained with the implementation of our algorithms.     

Detection of rumor conversations in Twitter using graph convolutional networks

Applied Intelligence - With the increasing popularity of the social network Twitter and its use to propagate information, it is of vital importance to detect rumors prior to their dissemination on...     

Analysis and characterization of comparison shopping behavior in the mobile handset domain

In this work we characterize the session-level behavior of users on an Indian mobile phone comparison shopping website. We also correlate the popularity of handset on various news sources to its popularity on the shopping website. There are three aspects to our study: data analysis, correlation between news sources of product information and popularity of a handset, and behavior prediction. We have used KL divergence to show that a time-homogeneous Markov chain is observed when the number of clicks varies from 5 to 30. Our results depict that Markov chain model does not hold in entirety for comparison shopping setting but tells us how far the Markov chain model holds for this setting. Our analysis corroborates intuition that increasing price leads to decrease in popularity. After the strong correlation between various variables and user behavior was found, we predict the users macro (the overall sales of handset) and micro behavior (whether a user will convert or exit the site) using Markov logic networks. Our predictive model validates the intuition that past browsing behavior is an important predictor for future behavior. Methodology of combining data analysis with machine learning is, in our opinion, a new approach to the empirical study of such data sets.     

一种利用SSL加强非Web应用安全性的新方法

安全套接字层协议(SSL)在信息安全领域的应用越来越广泛,但由于其对非Web应用的支持并不好,因而影响了其适用范围。本文提出了一种利用SSL协议加强Win32平台下非Web应用安全性的新方法,并给出了初步试验结果。     

嵌入式高清媒体播放机的设计与实现

随着高清节目的流行,高清媒体播放机也逐渐成为新一代的媒体播放设备。介绍了一种基于Sigma Designs公司SMP8634芯片为核心、采用嵌入式LINUX操作系统设计的高清媒体播放机。     

基于用户中心访问行为的多臂缓存方法

针对现有的边缘缓存策略无法有效预测短时热内容集和冷内容集流行度时变规律,而基于探索的多臂算法缺乏有效机制解决探索过程的过量探索问题,提出了基于用户中心访问行为的多臂缓存方法（MACB）。MACB利用用户中心访问上下文缩小群体访问偏好内容集,在此基础上采用多臂算法的探索开发过程,有效学习短时热内容集和冷内容集的内容流行度变化规律。实验采用了中国移动用户记录数据集,并与相关缓存算法进行对比。结果显示MACB在缓存击中率上均高于其他对比缓存方法,表明了MACB缓存方法的有效性和优越性。     

On Metrics to Distinguish Skype Flows from HTTP Traffic

Skype is a Voice over IP (VoIP) Internet application that is gaining huge popularity in recent years. A key point to Skype popularity is its capability to dynamically adapt itself to operate behind firewalls or network proxies. A common way adopted by Skype to delude these network devices is to use port 80, normally expected to comprise HTTP traffic. In this paper, we propose metrics and investigate statistical tests intended to clearly distinguish Skype flows from HTTP traffic. We validate our study using real-world experimental datasets gathered at a commercial Internet Service Provider (ISP). Our experimental results suggest that the proposed methodology may be seen as a promising building block towards a system to detect general protocol anomalies in HTTP traffic.     

结合项目流行度加权的协同过滤推荐算法

针对传统协同过滤算法中存在的流行度偏差问题,提出一种结合项目流行度加权的协同过滤推荐算法。在项目协同过滤算法的基础上,分析项目流行度和流行度差异对相似度的影响;设置流行度阈值,对大于该阈值的流行项目设计惩罚权重,降低其对项目间相似度的贡献。通过在MovieLens 1M和Epinion数据集上进行实验验证和对比,结果表明,所提算法的预测准确度和覆盖率均优于传统算法,有效提高了推荐的多样性和新颖性,一定程度上缓解了流行度偏差问题。