AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

On the Nature of Online Retrieval of Electronic Data

With the development of information technology,the online retrieval of remote electronic data has become an important method for investigative agencies to collect evidence.In the current normative documents,the online retrieval of electronic data is positioned as a new type of arbitrary investigative measure.However,study of its actual operation has found that the online retrieval of electronic data does not fully comply with the characteristics of arbitrary investigative measures.The root cause is its inaccurately defined nature due to analogy errors,an emphasis on the authenticity of electronic data at the cost of rights protection,insufficient effectiveness of normative documents to break through the boundaries of law,and superficial inconsistency found in the mechanical comparison with the nature of existing investigative measures causes.The nature of electronic data retrieved online should be defined according to different circumstances.The retrieval of electronic data disclosed on the Internet is an arbitrary investigative measure,and following procedural specifications should be sufficient.When investigators conceal their true identities and enter the cyberspace of the suspected crime through a registered account to extract dynamic electronic data for criminal activities,it is essentially a covert investigation in cyberspace,and they should follow the normative requirements for covert investigations.The retrieval of dynamic electronic data from private spaces is a technical investigative measure and should be implemented in accordance with the technical investigative procedures.Retrieval of remote“non-public electronic data involving privacy”is a mandatory investigative measure,and is essentially a search in the virtual space.Therefore,procedural specifications should be set in accordance with the standards of searching.

A Personalized Search Model Using Online Social Network Data Based on a Holonic Multiagent System

Personalized search utilizes user preferences to optimize search results,and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data.However,the behavioral data are noisy because users often clicked some irrelevant documents to find their required information,and the new user cold start issue represents a serious problem,greatly reducing the performance of personalized search.This paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results,mine the knowledge of user interests,user influence and user relationships from online social networks,and use this knowledge to optimize the results returned by search engines.The proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the model.The experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.

ED-SWE:Event detection based on scoring and word embedding in online social networks for the internet of people

Online social media networks are gaining attention worldwide,with an increasing number of people relying on them to connect,communicate and share their daily pertinent event-related information.Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People.In this paper,a novel Event Detection model based on Scoring and Word Embedding(ED-SWE)is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets.The proposed ED-SWE model can distill high-quality tweets,reduce the negative impact of the advent of spam,and identify latent events in the data streams automatically.Moreover,a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data.In order to further improve the performance of the Expectation-Maximization(EM)iteration algorithm,a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely.Finally,a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event.Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.

On the Digitization of Doctoral/Master’s Dissertations and Online Access to the Resources

With the rapid development of higher education, more and more people are entitled doctoral or master’s degrees, resulting in the considerable increase in doctoral dissertations and master’s theses. The application of IT technology renders the possibility to digitize those dissertations, which has contributed a lot to the construction of digital library. The characteristics and corresponding problems like property right, security and sharing will be discussed in this paper. And the paper makes a general introduction will be made to the specific ways adopted by the library of UESTC and its distribution of digitized dissertation resources.

基于信息觅食理论的消费者在线评论搜索行为研究

[目的/意义]本研究旨在从信息觅食理论出发,分析点评类软件消费者在线评论搜索行为的内在机理。[方法/过程]以信息觅食理论为基础,围绕信息线索、斑块模型和菜单模型构建了消费者在线评论搜索行为模型。采用问卷调查法收集352份有效样本数据,并利用结构方程模型对理论模型进行分析与检验。[结果/结论]评论内容质量、评论丰富性、评论效价以及评论者资信度4类信息线索均会正向显著影响消费者斑块收益感知,进而正向影响消费者在线评论搜索行为。此外,评论效价也会直接正向显著影响消费者在线评论搜索行为。本研究在理论层面深入揭示了消费者在线评论搜索行为的内在机理,延伸了信息觅食理论的研究情境与边界;在实践层面为点评类软件的功能优化及引导消费者有效搜索在线评论提供了相关建议。

网络新媒介能否提升个人投资者业绩:来自基金直播的证据

党的二十大报告明确提出“多渠道增加城乡居民财产性收入”的目标。本文基于个人投资者微观调查数据从基金直播的视角探讨了网络新媒介对个人投资业绩的影响及其作用机理。研究发现,观看直播购买基金让投资者更易在基金市场中盈利。机制分析表明,基金直播通过增进金融知识、提高投资分散化程度、形成止盈止损习惯、抑制追逐热点等路径提升个人投资业绩。异质性分析发现,基金直播对业绩的提升作用在老年群体、低月收入、缺乏投资经验以及落后数字金融发展地区等群体中更显著。拓展探讨发现,利用基金直播进行投资应避免过度娱乐化,关注直播内容质量和学习金融知识才是重中之重。本文对引导个人投资者科学化投资,发挥资本市场投资者的财富效应提供了参考依据。

国外研究者关于用户在线信息搜索中认知偏差的研究:一个系统性综述

本综述旨在揭示国外关于用户在线信息搜索中认知偏差的研究现状,以帮助其他学者对该领域有更加全面的认识。遵循系统综述思想,纳入来自8个学术数据库的104篇文献,分析出版趋势、理论基础、研究主题和研究方法4个维度。近年来,对用户在线信息搜索中认知偏差的研究逐渐增多,反映了学者们对该领域的日益关注。这些研究的基础理论主要来自心理学和传播学。相关研究主要关注认知偏差的类型、成因、认知偏差对信息搜索的影响和纠偏策略4个研究子主题。此外,我们观察到研究趋势表明实验法是当前研究中采用最频繁的研究方法。

现象图析学视角下在线健康信息替代搜寻行为机理研究

[目的/意义]探析现象图析学视角下在线健康信息替代搜寻行为的影响因素与生成机理,有助于丰富信息行为研究的理论范式,为解决替代搜寻问题提供相应的建议与参考。[方法/过程]基于意义建构理论模型框架,从“情景—鸿沟—桥梁—结果”4个维度,对在线健康信息替代搜寻行为进行模型要素解析,并利用现象图析学质性方法深入挖掘该行为的内在机理,构建在线健康信息替代搜寻行为机理关系模型。[结果/结论]研究结果揭示了在线健康信息替代搜寻行为机理可分为情景触发、条件控制、桥梁构建、结果反馈4个子机理,并明晰了其机理关系,为今后相关研究者对在线健康信息替代搜寻行为机理研究提供理论借鉴和参考。

中美投资者关注与石油市场的波动风险——基于互联网搜索视角

行为金融学理论和互联网的发展,为解析投资者关注与金融市场之间的微观机制提供新的视角。本文聚焦于中美投资者关注对石油市场风险的影响。首先,基于GARCH-MIDAS模型实现投资者关注对石油期现货市场波动的影响测度。其次,围绕中美贸易摩擦这一突发事件构建中美投资者关注度指标,通过DCC-GARCH-CoVaR模型测度突发事件对石油市场风险溢出的影响,并分析中国与美国投资者关注对石油市场风险影响的差异性。研究发现,中美投资者关注度变化将导致石油市场波动增强,影响石油市场的稳定性;相较中国投资者关注,美国投资者关注对石油市场的影响作用更为显著。此外,投资者对中美贸易摩擦的关注对石油期现货市场之间风险溢出具有显著影响,在高风险溢出时更为显著。相关研究有助于了解突发事件对石油市场影响的不确定性,为提升我国对世界石油市场突发性风险的应对能力提供参考。

基于SEIR模型的网络热搜话题传播仿真研究

网络热搜话题具有传播扩散现象,当前对于网络热搜话题的研究主要集中在传播效果评估、传播趋势预测、社会影响评价以及舆论引导等方面,而对于网络热搜话题的研究未能揭示传播动力学参数对于传播过程的影响。文中采用SEIR模型构建了网络热搜话题传播动力学模型,分析研究了网络平均度、不信任概率、接触后立即传播概率、感染率、治愈率、复发率等影响因素对模型的影响。

基于电压极差特征的储能电池组早期健康状态检测

为了更加高效地评估储能电池组的健康状态(SOH),提出一种基于电压极差特征的早期健康状态检测方法。首先基于大容量磷酸铁锂储能电池组开展循环老化试验,测量每次循环的电压极差信号,并从中提取关键时间点的电压特征;其次,基于皮尔逊(Pearson)相关系数及灰色关联度分析法(GRA)筛选与电池组老化高度相关的健康因子。最后,通过麻雀搜索算法(SSA)优化双向长短时记忆网络(BiLSTM)的超参数,搭建SSA-BiLSTM健康状态估计模型,实现储能电池组SOH评估;并结合常规机器学习算法验证了健康因子的有效性和估计模型的优越性。结果表明,所提取充放电静置30 min的电压极差特征能够有效反映电池组容量衰退趋势,多种模型验证下SOH估计误差均低于±0.8%。其中,本文所提出的SSA-BiLSTM模型均方根误差(RMSE)低至0.07%。因此该方法能够有效地对大容量储能电池组的SOH实现在线监测。

主动性人格对大学生网络利他行为的影响:网络积极反馈和生命意义感的中介作用

为探讨大学生主动性人格、网络积极反馈、生命意义感和网络利他行为之间的关系。研究选取850名大学生,采用主动性人格问卷、网络积极反馈量表、生命意义感量表和网络利他行为量表进行测试。结果发现:大学生主动性人格正向预测网络利他行为;网络积极反馈和生命意义体验在主动性人格与网络利他行为间起单独和链式中介作用;生命意义寻求对网络利他行为的预测作用不显著。这意味着未来可通过塑造主动性人格、增加网络积极反馈和提升生命意义体验的途径来提高大学生网络利他行为。

台湾地区术语建设及术语相关研究管窥

随着内地和台湾地区交流的日益密切和频繁,加强两岸术语研究工作的交流与互鉴变得尤为重要。文章对台湾地区术语建设的管理结构、历时发展、已有成果,两岸共同编纂术语工具书的合作成果,“乐词网”术语搜索及资源在线平台,两岸共同建设的“中华语文知识库”及其他语料库进行了详细介绍和全面梳理。对台湾地区在Web of Science(WOS)核心合集数据库中与术语相关的研究进行了主题抽样分析,借助文献计量学工具VOSviewer进行了可视化呈现。揭示了台湾地区学者在国际核心期刊上发表的术语相关研究的发展趋势和热点议题。以期为众多两岸术语研究者、语言爱好者提供研究与学习的素材和途径,助力两岸学者的沟通与合作,并确定未来协作努力的方向,也为两岸的术语建设、制定科技发展战略提供有益的参考和支撑。

投资者关注度对公司股价崩盘风险的影响

资本市场稳定对经济社会大局稳定具有重大意义,而如何促进资本市场稳健发展一直备受关注。本文选取我国2011—2022年A股市场数据,利用上市公司百度搜索指数,实证检验投资者关注度在股价崩盘风险中的影响效应和传导途径。研究发现,投资者关注程度的提升对股价崩盘风险有明显的抑制作用,且股票流动性在其中发挥了中介效应的作用。本文分析结论有利于进一步厘清市场投资者与股市崩盘风险之间的关联,为维护资本市场稳定提供政策启示。

基于无监督深度融合机制的货物在线装箱算法

目的针对当前三维装箱算法存在的模型鲁棒性差、泛化性弱、装载率低等问题,设计一种无监督融合机制的在线装箱算法。方法充分考虑货物“即到即码”的实时性需求,以容器空间利用率为优化目标,基于无监督深度融合指针网络端到端学习模型框架,将在线三维装箱的码垛过程公式化地表述为马尔科夫决策过程,设计强化学习要素,并以深度强化学习算法为主,融入蒙特卡洛树搜索,对智能体的决策动作进行训练,以生成具有较优“学习”能力的在线三维装箱模型。结果采用125种不同尺寸和方向随机生成货物数据集,并在7种约束条件下验证,实验结果表明,容器的平均利用率可达84.6%。结论该算法的泛化性较好,且其装载率远优于当前效果较好的启发式算法、深度学习方法,为货物的在线装箱提供了理论依据及参考。

融合情景分析与多维技术创新地图的专利群规避设计——基于在线评论驱动的视角

[研究目的]专利规避是企业突破技术壁垒、提升竞争优势并获取领先地位的重要武器。针对已有研究多局限于产品技术系统功能设计,缺乏对用户需求、产品使用环境和未来不确定性的考虑,以及规避设计求解过程繁琐等,提出一种在线评论驱动下融合情景分析与多维技术创新地图的专利群规避设计方法。[研究方法]首先,通过在线评论分析用户需求,并深入挖掘产品在未来使用情景中潜在的隐性需求;其次,利用SAO语义分析、多维技术创新地图及功能导向搜索分别构建目标专利群和知识源专利的三级语义知识库;再次,通过知识迁移识别创新要素,利用多维技术创新地图的维-法耦合原理生成系列专利群规避设计方案。最后,以电动滑板车领域的专利群规避设计研究为例验证了本方法的可行性。[研究结论]结果表明,本研究所提方法能够帮助企业高效开展专利规避设计,加速产品迭代和技术进步。

线上投资者关系管理:文献回顾与研究展望

随着互联网传播技术的飞速发展,网络平台逐渐成为投资者信赖的信息渠道,日益成为影响上市公司股价的重要因素,线上投资者关系管理也由此成为上市公司市值管理工作的重点。文章以影响因素研究与经济后果研究两大分支为主要脉络,对相关文献进行系统梳理,同时总结现有研究,关于线上投资者关系管理代理变量的选择与度量,总结出现有研究的不完善之处,并在此基础之上形成研究述评,以期为未来相关领域的研究提供启发。

基于离散监督哈希算法的非结构化网络数据库在线检索方法

受非结构化网络数据关联性弱、数据收敛特性差的影响,对相关数据库进行检索时,检索精度难以得到保障,为此,提出基于离散监督哈希算法的非结构化网络数据库在线检索方法研究。首先,基于离散监督哈希算法将非结构化网络数据样本的显式语义标签利用相应的二进制码进行表示,并借助拉格朗日乘子对其进行离散化处理,使得哈希函数对于学习非结构化网络数据显式语义关联快速收敛。然后,按照相同的方式对所有数据及查询请求进行编码。最后,将与查询请求哈希编码匹配程度最高的数据作为优先检索输出结果。在测试结果中,设计检索方法在图像查询文本指令,以及文本查询图像指令下的检索平均精度均值始终稳定在较高水平,且检索时间最高仅为7 s,具有实用性。

投资者情绪的跨市场溢出效应——基于股票和商品期货网络搜索指数的研究

研究投资者情绪在股票和商品期货市场的溢出效应,对于投资者更好地理解资本市场的网络结构以及有效规避投资风险具有一定的理论和实践意义。采用百度搜索指数作为投资者情绪的代理变量,可以有效统一两个市场的衡量标准,避免了以往研究标准无法统一的问题。通过对投资者情绪的跨市场溢出效应进行检验,得出以下结论:投资者情绪对跨市场的资产收益率及资产波动率均存在显著的影响;两个市场的投资者情绪存在着显著的波动溢出现象,且股价下跌后股票投资者情绪对商品期货市场的波动溢出变得更为显著。