基于问答（Q＆A）系统原理的WAP信息检索

移动网络为人们提供了越来越丰富的信息服务，然而，这些面向移动终端的信息服务却缺少一个有效的信息检索途径，即用户无法对这些海量的信息进行快速、方便的检索以满足信息使用要求。本文以此为问题出发点．提出了基干问答（Q＆A）原理的WAP信息检索方案，并就其如何解决无线网络信息检索问题进行了相关论述．     

Investigating the lack of diversity in user behavior: The case of musical content on online platforms

Whether to deal with issues related to information ranking (e.g. search engines) or content recommendation (on social networks, for instance), algorithms are at the core of processes that select which information is made visible. Such algorithmic choices have a strong impact on users’ activity de facto, and therefore on their access to information. This raises the question of how to measure the quality of the choices algorithms make and their impact on users. As a first step in that direction, this paper presents a framework with which to analyze the diversity of information accessed by users in the context of musical content.The approach adopted centers on the representation of user activity through a tripartite graph that maps users to products and products to categories. In turn, conducting random walks in this structure makes it possible to analyze how categories catch users’ attention and how this attention is distributed. Building upon this distribution, we propose a new index referred to as the (calibrated) herfindahl diversity, which is aimed at quantifying the extent to which this distribution is diverse and representative of existing categories.To the best of our knowledge, this paper is the first to connect the output of random walks on graphs with diversity indexes. We demonstrate the benefit of such an approach by applying our index to two datasets that record user activity on online platforms involving musical content. The results are threefold. First, we show that our index can discriminate between different user behaviors. Second, we shed some light on a saturation phenomenon in the diversity of users’ attention. Finally, we show that the lack of diversity observed in the datasets derives from exogenous factors related to the heterogeneous popularity of music styles, as opposed to internal factors such as recurrent user behaviors.     

互联网学术信息质量启发式评价中存在的问题分析

从用户的视角探讨互联网学术信息质量启发式评价中存在的问题。研究发现,图书馆等服务人员要加强互联网信息资源评价服务的宣传和覆盖率,并提高评价工具的可操作性。部分用户的评价能力欠缺影响启发式工具的使用效果,但大部分用户具备一定的评价Web学术信息质量的能力,许多高校学生在信息查寻行为中没有或很少使用启发式原则或标准的症结在于评价动机而不在于评价能力。     

通过Ｐowerize.com免费获取工商金融情报

Powerize. com is well known for providing free business and financial information on the Web. The unique information integrated service of Powerize. com, its approaches to free information and fee-based information as well as its searching modes are discussed. The author suggests that information experts pay more attention to recommending such kinds of services to Intemet users in China in order to let more and more Intemet users obtain free information via such kinds of services.     

A semantic Bayesian network approach to retrieving information with intelligent conversational agents

As access to information becomes more intensive in society, a great deal of that information is becoming available through diverse channels. Accordingly, users require effective methods for accessing this information. Conversational agents can act as effective and familiar user interfaces. Although conversational agents can analyze the queries of users based on a static process, they cannot manage expressions that are more complex. In this paper, we propose a system that uses semantic Bayesian networks to infer the intentions of the user based on Bayesian networks and their semantic information. Since conversation often contains ambiguous expressions, the managing of context and uncertainty is necessary to support flexible conversational agents. The proposed method uses mixed-initiative interaction (MII) to obtain missing information and clarify spurious concepts in order to understand the intention of users correctly. We applied this to an information retrieval service for websites to verify the usefulness of the proposed method.     

基于本体的单元信息知识库构建研究

构建单元信息知识库,可以实现对单元信息的有效揭示、组织与利用,为实现精粹服务的需求提供知识保障。本文引入本体提出了单元信息知识库的构建方法,设计了知识库的总体结构,构建了单元信息本体,阐述了数据建设的过程,并以"养生领域"为例实现了知识库的构建。本文所构建的基于本体的单元信息知识库能够较好地组织、存储和利用单元信息,提供知识检索和知识发现服务,实现从海量数据中有效获取有价值信息的需求。     

基于用户行为感知的移动社交网络信息服务持续使用意愿研究

[目的/意义]本文以用户行为感知视角，研究影响移动社交网络主导型用户与浏览型用户持续使用的因素，为移动社交网络信息服务提供理论基础，并为移动社交网络提供商提出参考与应用借鉴。[方法/过程]分析移动社交网络主导型用户与浏览型用户持续使用意愿影响因素，引入相关变量，构建了基于用户行为感知的移动社交网络信息服务持续使用意愿模型并提出假设，最后通过结构方程模型进行实证分析。[结果/结论]感知有用性、感知易用性、感知娱乐、感知质量等因素均显著影响主导型及浏览型两类用户；服务质量、感知风险、知识获取、个人创新、社会认可、感知信任、感知转换成本等因素对两类用户有不同程度的影响。     

An information push-delivery system design for personal information service on the Internet

As Web-related techniques and equipment grow, the Internet has become popular as a major channel for providing a wide variety of information. However, users face the serious problem of information overload when acquiring increasing amounts of information from the Internet. This problem is one of the most important issues in providing information services to meet users’ requirements in an electronic commerce environment. In this paper, we propose an information push-delivery system, which applies fuzzy information retrieval and fuzzy similarity measurement to avoid the information overload problem. This proposed system is helpful for users to acquire suitable information from the Internet. An empirical investigation of the proposed system is implemented in this study. The results show that the degree of satisfaction for the received information for all participants was as high as 71%, indicating that the proposed system can effectively provide correct and interesting information for users.     

Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor

Analyzing and extracting insights from user-generated data has become a topic of interest among businesses and research groups because such data contains valuable information, e.g., consumers’ opinions, ratings, and recommendations of products and services. However, the true value of social media data is rarely discovered due to overloaded information. Existing literature in analyzing online hotel reviews mainly focuses on a single data resource, lexicon, and analysis method and rarely provides marketing insights and decision-making information to improve business’ service and quality of products. We propose an integrated framework which includes a data crawler, data preprocessing, sentiment-sensitive tree construction, convolution tree kernel classification, aspect extraction and category detection, and visual analytics to gain insights into hotel ratings and reviews. The empirical findings show that our proposed approach outperforms baseline algorithms as well as well-known sentiment classification methods, and achieves high precision (0.95) and recall (0.96). The visual analytics results reveal that Business travelers tend to give lower ratings, while Couples tend to give higher ratings. In general, users tend to rate lowest in July and highest in December. The Business travelers more frequently use negative keywords, such as “rude,” “terrible,” “horrible,” “broken,” and “dirty,” to express their dissatisfied emotions toward their hotel stays in July.     

Information avoidance behavior on social network sites: Information irrelevance,overload, and the moderating role of time pressure

Drawing on the stressor-strain-outcome framework, this study investigates how information irrelevance and overload induce social network fatigue, and the relationship of these variables to users’ information avoidance behavior. It also examines the conditions under which social network fatigue is more likely to be translated into information avoidance behavior. The analysis of data collected from 341 users of WeChat Moments suggests that information irrelevance directly leads to information avoidance behavior, and social media fatigue as a mediator partially mediates the impact of information overload on information avoidance behavior and fully mediates the impact of social overload on information avoidance behavior. Furthermore, time pressure strengthens the effect of social network fatigue on information avoidance behavior. This study fulfills the identified need for an in-depth investigation of actual discontinuous behavior in social network services (SNSs) by investigating information avoidance behavior and its antecedents. The findings provide SNSs providers with guidelines on how to manage users’ behavior so that they remain active users of the SNSs.     

轻量化视角下微信小程序用户持续使用意愿研究

以微信小程序为代表的轻量应用一定程度上改变了企业与用户之间的互动方式,如何提高“用户留存”已经成为企业进行社交媒体营销所面临的重要问题。在社交媒体情境下,小程序具有哪些特征、这些特征是否以及如何影响用户的持续使用意愿,并未得到理论界的足够重视。本研究从轻量化视角深入分析,基于信息系统成功模型与情绪理论构建了小程序轻量化特征影响用户持续使用意愿的理论模型。研究表明：信息-任务匹配、系统易用性以及服务响应性正向影响用户持续使用意愿;信息-任务匹配、服务响应性分别对用户积极情绪和流体验有促进作用,系统易用性正向影响用户流体验而对积极情绪的影响不显著;积极情绪分别在信息-任务匹配、服务响应性与用户持续使用意愿之间起到完全中介作用,流体验则完全中介了小程序轻量化特征与持续使用意愿之间的关系。研究构筑了“轻量化特征-情绪反应-行为意愿”完整的传导机制与影响路径,拓展了轻量应用持续使用、社交媒体用户情绪成因相关研究,结论对企业借助轻量应用进行社交媒体营销具有借鉴意义。     

The role of location and social strength for friendship prediction in location-based social networks

Recent advances in data mining and machine learning techniques are focused on exploiting location data. These advances, combined with the increased availability of location-acquisition technology, have encouraged social networking services to offer to their users different ways to share their location information. These social networks, called location-based social networks (LBSNs), have attracted millions of users and the attention of the research community. One fundamental task in the LBSN context is the friendship prediction due to its role in different applications such as recommendation systems. In the literature exists a variety of friendship prediction methods for LBSNs, but most of them give more importance to the location information of users and disregard the strength of relationships existing between these users. The contributions of this article are threefold, we: 1) carried out a comprehensive survey of methods for friendship prediction in LBSNs and proposed a taxonomy to organize the existing methods; 2) put forward a proposal of five new methods addressing gaps identified in our survey while striving to find a balance between optimizing computational resources and improving the predictive power; and 3) used a comprehensive evaluation to quantify the prediction abilities of ten current methods and our five proposals and selected the top-5 friendship prediction methods for LBSNs. We thus present a general panorama of friendship prediction task in the LBSN domain with balanced depth so as to facilitate research and real-world application design regarding this important issue.     

Media Literacy and Universal Access in Europe

The promotion of media literacy as way of increasing access to the range of services available via today's media and communication technologies is currently an emphasis in Europe's information society policy debates. The notion of media literacy heralds a shift in the communications policy arena, especially with regard to media access as a policy goal. Taking into account the situated origins of the inherited regulatory concepts of access, this article argues that the way in which we operationalize media access must reflect how individuals engage with convergent electronic media services. It proposes a context- and user-sensitive approach, where the situation of media (non)users is assessed in terms of the technological and social infrastructure needed to support their access to particular media services.     

面向企业的科技查新信息管理与服务系统

随着企业技术创新能力的不断提高和信息化建设进程的不断加快,科研课题数量不断增加。科技查新作为科研成果管理工作中的重要环节,用户对获取科技查新信息的手段、方式、渠道、时空也有了新的要求。国网信息通信有限公司利用先进的信息通信技术构建了集互联网信息、电话语音信息、短信信息、手机信息于一体的信息管理与服务系统,在信息资源和用户之间形成了一个整体的科技查新技术支持环境与服务环境。该系统充分利用科技查新资源的优势和资源整合的优势,将信息的管理与服务有机地融合在一起,为用户提供更多、更快、更好的数字信息资源服务。     

Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approach

Modeling discussions on social networks is a challenging task, especially if we consider sensitive topics, such as politics or healthcare. However, the knowledge hidden in these debates helps to investigate trends and opinions and to identify the cohesion of users when they deal with a specific topic. To this end, we propose a general multilayer network approach to investigate discussions on a social network. In order to prove the validity of our model, we apply it on a Twitter dataset containing tweets concerning opinions on COVID-19 vaccines. We extract a set of relevant hashtags (i.e., gold-standard hashtags) for each line of thought (i.e., pro-vaxxer, neutral, and anti-vaxxer). Then, thanks to our multilayer network model, we figure out that the anti-vaxxers tend to have ego networks denser (+14.39%) and more cohesive (+64.2%) than the ones of pro-vaxxer, which leads to a higher number of interactions among anti-vaxxers than pro-vaxxers (+393.89%). Finally, we report a comparison between our approach and one based on single networks analysis. We prove the effectiveness of our model to extract influencers having ego networks with more nodes (+40.46%), edges (+39.36%), and interactions with their neighbors (+28.56%) with respect to the other approach. As a result, these influential users are much more important to analyze and can provide more valuable information.     

WSN数据管理平台

提出一种基于C/S架构的高能效数据管理平台,其设计是基于.NetFramework3.5和SQLServer,通过对数据进行分析和图表化处理,使用户可以方便、直观地获取传感器采集的数据,并对传感网进行实时监控。同时,对数据查询和显示进行了算法优化,节省了资源和能耗。测试结果表明,该平台运行状况良好,数据监测正常无误,使用的优化算法也达到良好效果。     

基于移动社交平台的用户信任度分析研究

随着现代网络的发展,登录移动社交平台已经成为大多数人每天的日常,和亲人朋友在社交平台上的交流远远多于面对面的交谈,学习工作上的事情也大多可以用社交软件完成,在这样的大前提下,移动社交平台用户之间的信任关系必然要成为关注的重点。用户信任度可以用来详细检查用户之间所有可能的社交网络关系,本文以新浪微博为例提供了一种计算用户之间信任的方法,通过对用户之间的信任进行分析完成相应的推荐和其他服务。     

Marketing analysis of wineries using social collective behavior from users’ temporal activity on Twitter

Marketing professionals face challenges of increasing complexity to adapt classic marketing strategies to the phenomenon of social networks. Companies are currently trying to take advantage of the useful collective knowledge available on social networks to support different types of marketing decisions. The appropriate analysis of this information can offer marketing professionals with important competitive advantages. This work proposes a new methodology to extract the social collective behavior of Twitter users concerning a group of brands based on the users’ temporal activity. Time series of mentions made by individual users to each company’s Twitter account are aggregated to obtain collective activity data for the companies, which is a consequence of both the company’s and other users’ actions. These data are processed using classical unsupervised machine learning techniques, such as temporal clustering and hidden Markov models, to extract collective temporal behavior patterns and models of the dynamics of customers over time for a single brand and groups of brands. The derived knowledge can be used for different tasks, such as identifying the impact of a marketing campaign on Twitter and comparatively assessing the social behaviors of different brands and groups of brands to assist in making marketing decisions. Our methodology is validated in a case study from the wine market. Twitter data were gathered from four regions of different countries around the world with important wineries (Italy: Veneto, Portugal: Porto and Douro Valley, Spain: La Rioja, and United States: Napa Valley), and comparative behavior analysis was carried out from the perspective of the use of Twitter as a communication channel for marketing campaigns.     

数字图书馆图像资源检索框架的构建与实现——基于非下采样的Contourlet变换

图像检索为数字图书馆的发展提供了技术支持,图书馆应重视数字化发展以提升服务质量。本文提出一种数字图书馆图像资源检索框架,并对系统的实现过程做了详细的分析。同时,在提取图像特征时提出了一种基于非下采样的Contourlet变换图像检索算法（NSCT）,能够在大量图像数据中挖掘有效的特征信息。该算法首先对图像进行多尺度、多方向分解,然后计算低频和高频中不同方向的子带系数的标准差和均值作为图像的纹理特征。实验结果显示,本文提出的图像检索框架具有可行性,能够为用户提供更优质的搜索服务,并且与同类特征提取算法进行比较,该算法具有良好的检索性能和较高的查准率、查全率。     

NRAND: An efficient and robust dismantling approach for infectious disease network

Disease spread control is a challenging task with growing importance in recent years. Infectious disease networks have been proven to be a helpful resource for controlling the epidemic by targeting a smaller population. However, the information on these networks is often imprecise, diffused, concealed, and misleading, making it challenging to obtain a complete set of real-world data, i.e., some links might be missing, which can be a risk to the widespread of the pandemic. The former studies on infectious disease networks ignore the influence of neighborhood missing links in the infectious disease network topology, thus massively targeting the irrelevant population, resulting in poor epidemic control performance. In this paper, to address such a problem, we study how a small portion of the population should be targeted with incomplete network information to effectively prevent the pandemic. We propose an algorithm, namely, the Neighborhood Relation Aware Network Dismantling Algorithm (NRAND), to efficiently address the infectious disease network’s dismantling problem. For comparison, four network dismantling strategies are employed in our experiments. An extensive empirical study of real-world networks suggests that the proposed algorithm NRAND’s dismantling performance is significantly greater than the state-of-the-art algorithms, indicating that NRAND can be a smarter option for dismantling real-world infectious disease networks.