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.

Database Search Behaviors: Insight from a Survey of Information Retrieval Practices

This study examines the database search behaviors of individuals, focusing on gender differences and the impact of planning habits on information retrieval. Data were collected from a survey of 198 respondents, categorized by their discipline, schooling background, internet usage, and information retrieval preferences. Key findings indicate that females are more likely to plan their searches in advance and prefer structured methods of information retrieval, such as using library portals and leading university websites. Males, however, tend to use web search engines and self-archiving methods more frequently. This analysis provides valuable insights for educational institutions and libraries to optimize their resources and services based on user behavior patterns.

Investigating the relationships between facets of work task and selection and query-related behavior

Purpose: This study aims to explore the relationships between different facets of work task and selection and query-related behavior.Design/methodology/approach:An experiment was conducted to explore the issue. The researcher recruited 24 participants and assigned six simulated work task situations to each of them. Each experiment lasted around 2 hours and was recorded by the software tool Morae.Findings: Time(frequency) and time(length) are more closely related to user’s selection and query-related behavior compared to the facet ‘process’ of work task. Knowledge level of work task topic, degree of work task difficulty, and subjective work task complexity are significantly correlated with selection and query-related behavior. Work task difficulty and work task complexity are different concepts. Subjective work task complexity, work task difficulty, and knowledge of work task topic are significantly correlated with user’s selection and query-related behavior.Research limitations/implications: The limitations of this study include a small sample size,limited work task situations, and possible spurious relationships. This study has implications in informing task-based information seeking/search/retrieval research and interactive information retrieval(IIR) systems design.Originality/values: Previous studies usually did not touch upon how different facets of work tasks affected interactive activities. Some studies examining task complexity and information behavior were concerned with how work tasks affect users’ behavior at information-seeking level, rather than at information search level. This study makes contribution to interactive information retrieval,task-based information search and retrieval, and personalization of IR.

Improved Transient Search Optimization with Machine Learning Based Behavior Recognition on Body Sensor Data

Recently,human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart healthcare systems.Even though there are various forms of utilizing distributed sensors to monitor the behavior of people and vital signs,physical human action recognition(HAR)through body sensors gives useful information about the lifestyle and functionality of an individual.This article concentrates on the design of an Improved Transient Search Optimization with Machine Learning based BehaviorRecognition(ITSOMLBR)technique using body sensor data.The presented ITSOML-BR technique collects data from different body sensors namely electrocardiography(ECG),accelerometer,and magnetometer.In addition,the ITSOML-BR technique extract features like variance,mean,skewness,and standard deviation.Moreover,the presented ITSOML-BR technique executes a micro neural network(MNN)which can be employed for long term healthcare monitoring and classification.Furthermore,the parameters related to the MNN model are optimally selected via the ITSO algorithm.The experimental result analysis of the ITSOML-BR technique is tested on the MHEALTH dataset.The comprehensive comparison study reported a higher result for the ITSOMLBR approach over other existing approaches with maximum accuracy of 99.60%.

Adaptive Expanding Ring Search Based Per Hop Behavior Rendition of Routing in MANETs

Routing protocols in Mobile Ad Hoc Networks(MANETs)operate with Expanding Ring Search(ERS)mechanism to avoid ooding in the network while tracing step.ERS mechanism searches the network with discerning Time to Live(TTL)values described by respective routing protocol that save both energy and time.This work exploits the relation between the TTL value of a packet,trafc on a node and ERS mechanism for routing in MANETs and achieves an Adaptive ERS based Per Hop Behavior(AERSPHB)rendition of requests handling.Each search request is classied based on ERS attributes and then processed for routing while monitoring the node trafc.Two algorithms are designed and examined for performance under exhaustive parametric setup and employed on adaptive premises to enhance the performance of the network.The network is tested under congestion scenario that is based on buffer utilization at node level and link utilization via back-off stage of Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA).Both the link and node level congestion is handled through retransmission and rerouting the packets based on ERS parameters.The aim is to drop the packets that are exhausting the network energy whereas forward the packets nearer to the destination with priority.Extensive simulations are carried out for network scalability,node speed and network terrain size.Our results show that the proposed models attain evident performance enhancement.

A preliminary study on exploratory search behavior of undergraduate students in China

Purpose: This study attempts to investigate how a user's search behavior changes in the exploratory search process in order to understand the characteristics of the user's search behavior and build a behavioral model.Design/methodology/approach: Forty-two matriculated full-time senior college students with a female-to-male ratio of 1 to 1 who majored in medical science in Jilin University participated in our experiment. The task of the experiment was to search for information about 'the influence of environmental pollution on daily life' in order to write a report about this topic. The research methods include concept map, query log analysis and questionnaire survey.Findings: The results indicate that exploratory search can significantly change the knowledge structure of searchers. As searchers were moving through different stages of the exploratory search process, they experienced cognitive changes, and their search behaviors were characterized by quick browsing, careful browsing and focused searching.Research limitations: The study used only one search topic, and there is no comparision or control group. Although we took search habits, personal thinking habits, personality characteristics and professional background into account, a more detailed study to analyze the effects of these factors on exploratory search behavior is needed in our further research.Practical implications: This study can serve as a reference for other researchers engaged in the same effort to construct the supporting system of exploratory search.Originality/value: Three methods are used to investigate the behavior characteristics during exploratory search.

Chinese college students' Web querying behaviors:A case study of Peking University

This study examined users' querying behaviors based on a sample of 30 Chinese college students from Peking University. The authors designed 5 search tasks and each participant conducted two randomly selected search tasks during the experiment. The results show that when searching for pre-designed search tasks, users often have relatively clear goals and strategies before searching. When formulating their queries, users often select words from tasks, use concrete concepts directly, or extract 'central words' or keywords. When reformulating queries, seven query reformulation types were identified from users' behaviors, i.e. broadening, narrowing, issuing new query, paralleling, changing search tools, reformulating syntax terms, and clicking on suggested queries. The results reveal that the search results and/or the contexts can also influence users' querying behaviors.

Tourism demand forecasting and tourists’search behavior:evidence from segmented Baidu search volume

Given the importance of web search volume for reflecting tourists'preferences for certain tourism services and destinations,incorporating these data into forecasting models can significantly improve forecasting performance.This study enriches the literature on tourism demand forecasting and tourists'search behavior through segmented Baidu search volume data.First,this study divides Baidu search volume data based on volume sources and periods.Then,by analyzing the most relevant keywords in tourism demand in different segments,this study captures the dynamic characteristics of tourist search behavior.Finally,this study adopts a series of econometric and machine learning models to further improve the performance of tourism demand and forecasting.The findings indicate that tourists’search behavior has changed significantly with the prevalence and popularization of 4G technology and suggest that search volume improves forecasting performance,especially search volume on mobile terminals,from 2014M1–2019M12.

真实工作场景下“搜索即学习”任务集的构建与分析

文章采用日记法收集来自16个国民经济行业的真实工作场景下具有学习特征的搜索任务实例,结合任务概念框架对搜索任务进行表征,构建面向“搜索即学习”研究的多维任务集,并进一步剖析了真实工作场景下搜索任务的属性特征,对任务相关特征、参与者认知变化、行为及关系进行探讨,根据参与者的学历层次、完成任务的认知层次,以及在完成任务时所处的时间压力进行分组比较三组变量间的关系。研究发现:参与者的任务执行熟悉度与任务感兴趣程度,所需信息类型的数量显著影响认知变化,任务执行者特征对认知变化与行为的影响在不同情境下均存在显著差异。本研究开发的任务集可作为“搜索即学习”研究的公共研究资源,以及评估生成式人工智能技术复杂任务处理能力的评测集,为开发面向真实工作任务情境与高效的信息系统提供依据。

红火蚁对亚洲玉米螟的捕食作用及其发生影响

蚂蚁在蛀茎害虫的综合治理方面发挥着重要的作用。全球分布性的入侵蚂蚁红火蚁取食多种无脊椎动物并在入侵区域成为优势物种。但是目前尚不清楚红火蚁对玉米重要害虫亚洲玉米螟的捕食作用。本文在实验室和野外条件下研究红火蚁对亚洲玉米螟的觅食和捕食行为。研究显示,对于隐藏度低的亚洲玉米螟,红火蚁发现该害虫的时间约为40s,召集时间少于1 h。对于隐藏度高的亚洲玉米螟,红火蚁发现该害虫的时间超过26 min,召集时间接近6 h。野外实验显示,红火蚁可以降低玉米中后期生长阶段中50%的亚洲玉米螟卵块、94.1%的亚洲玉米螟幼虫和92.9%亚洲玉米螟蛀食孔。研究提供更多的视角理解红火蚁对生物多样性影响。

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。

基于演化动力学的老年人在线健康信息搜寻行为研究

[目的/意义]分析老年人在线健康信息搜寻现状,掌握其热点主题及演化趋势,对满足和提高老年人健康信息需求与健康素养水平,推动老年健康服务高质量发展具有重要意义。[方法/过程]本研究采用DTM模型对2016-2023年间新浪微博发文内容进行动态主题挖掘与分析,分别从主题演化、主题语义演化和主题信息熵趋势等方面进行研究。[结果/结论]“老年病症”“科技养老”“食疗保健”“心理健康”及“社会关怀”等方面主题演化显著,老年人对老年常见病、身体医疗养护、社会助老爱老关怀和衣食住行等健康信息类型关注颇多,用于满足需求和获取信息。“老年病症”“运动保健”“高危风险”及“医疗诈骗”等主题语义稳定。“运动保健”“起居安全”及“病毒传播”等信息熵趋势较为稳定,“医疗素养”“疫情管控”“文体旅游”及“饮食均衡”等信息熵呈现扩散趋势,“高危风险”“食疗保健”“经济陷阱”及“医疗诈骗”等信息熵呈现收敛趋势。

高校毕业生未来时间洞察力对求职行为的影响——基于职业规划能力和职业决策自我效能感的作用机制

以天津市高校毕业生为例,实证分析了未来时间洞察力、职业决策自我效能感、职业规划能力与毕业生求职行为的关系。研究表明:高校毕业生的未来时间洞察力、职业决策自我效能感、职业规划能力、求职行为之间两两显著正相关。高校毕业生未来时间洞察力可以直接预测求职行为,也可以通过职业决策自我效能感的中介作用间接影响求职行为,职业规划能力在其中有负向调节作用。

“互联网+”时代背景下智慧课堂交互行为数据精准搜索方法

在智慧课堂环境中,交互行为数据的复杂性难以保障准确的搜索结果。因此,在“互联网+”时代背景下,文章提出对智慧课堂交互行为数据进行精准搜索的方法并构建了一个智慧课堂交互行为数据分类模型,从“人-人交互”和“人-内容交互”2个维度深入分析智慧课堂交互行为数据的构成。为实现精准搜索,文章设置了带有唯一标签的智慧课堂交互行为数据染色体,基于目标对象的唯一标签,精准地确定搜索结果。测试结果显示,该方法能够显著提升交互行为数据的搜索精准性,与对照组相比具有明显优势,查全率偏差基本稳定在5.0%以内,最大偏差仅为6.0%,最小偏差达到了0。

生涯决策模糊容忍度对大学生求职行为的影响:一个有调节的中介模型

本研究基于社会认知职业理论探讨了模糊容忍度和大学生求职行为的关系及其作用机制。采用模糊容忍度量表、未来时间知觉量表、未来工作自我量表以及求职行为量表对731名大学生进行问卷调查。结果表明:(1)模糊容忍能够显著正向预测大学生的求职行为;(2)未来工作自我在模糊容忍与求职行为之间起部分中介作用;(3)未来时间知觉调节了该中介模型的前半段路径和直接路径。

搜索引擎平台优化策略何以提升消费者福利?

本文通过构造搜索引擎平台优化策略的一般性分析框架,及与之相匹配的两阶段理论模型,考察搜索引擎平台优化策略的作用机理,探索搜索引擎平台优化策略对消费者福利及搜索引擎平台利润的影响,从而推动搜索引擎平台优化策略的有效应用。研究发现:短期来看,搜索引擎平台优化策略的排序均衡将违背消费者检索意愿,虽激励了低质量销售商参与付费位置拍卖活动,维持付费位置拍卖市场稳定性,增加搜索引擎平台利润,却损害了消费者福利;长期来看,搜索引擎平台优化策略的排序均衡将契合消费者检索意愿,激励高质量销售商最大限度吸引消费者注意力,推动消费者实施购买行为,提升消费者福利,并通过降低均衡投标金额,一定程度地激励低质量销售商持续参与付费位置拍卖活动,进而实现协调双边用户的需求。并且,搜索引擎平台优化策略的排序均衡对搜索引擎平台利润有“先降后升”的“U”型曲线的影响。产业实践案例显示,中文主流搜索引擎平台的优化策略遵循长短期效应。

知识服务平台用户检索与推荐交互行为关系研究

[目的/意义]“推荐交互行为”特指用户在信息检索过程中与系统提供的推荐功能进行交互的行为。揭示用户检索行为与推荐交互行为之间的关系,为改善知识服务平台的推荐算法提供实证参考。[方法/过程]以CNKI和ScienceDirect为实验平台,采用实验研究方法开展用户研究,招募36位实验参与者,利用Morae软件采集用户的交互行为数据,并进行分析。[结果/结论] 6种检索行为与用户推荐交互行为具有相关性,且构建查询、浏览文献详情页面、在线阅读文献及下载文献对用户的推荐交互行为具有不同程度的影响,包括影响用户使用推荐次数、浏览推荐文献详情页面、在线阅读推荐文献和使用推荐总时长。

互通立交合流区驾驶人视觉特性以及加速车道形式影响研究

为研究驾驶人在互通立交合流区域的视觉特性及其影响因素,开展了47位被试的实车驾驶试验,利用k-means动态聚类算法划分了驾驶人经过立交合流区时的视窗区域,并选择注视时间、扫视幅度和注视目标占比等作为评价驾驶人视觉特性的指标,分析加速车道形式对驾驶人视觉特性的影响。结果表明:驾驶人的注视视窗主要集中在6个区域。在加速车道段,驾驶人对前方道路近处的注视需求最高,加速车道长度不足时,会增加驾驶人对左侧车辆的关注,其水平扫视幅度更为广泛;在直接式加速车道时,驾驶人对前方道路及车辆目标的注视需求较高;在平行式加速车道时更关注道路左侧和设施信息,即护栏和交通标志信息。

基于混合策略改进ASO-LSSVM的风险驾驶行为分类识别

为解决现有智能算法在优化支持向量机识别风险驾驶行为过程中收敛速率缓慢和误差较大的问题。首先,采用Tent映射取代原子搜索优化算法(ASO)种群初始化随机设置的方式,增加原子种群多样性和质量;其次,使用逐维小孔成像反向学习与柯西变异混合机制,提高原子个体择优位置的多样性,克服ASO算法易陷入局部最优和过早收敛的问题;最后,通过引入自适应变螺旋搜寻策略改进原子个体位置更新过程,以提升ASO算法的全局搜索能力,实现全局搜索和局部开发间关系的有效平衡,缓解ASO算法易陷入局部最优和收敛精度不足的问题。以上海北横通道出口匝道车辆轨迹数据为输入,使用混合策略改进ASO算法寻优求解最小二乘支持向量机(LSSVM)参数,构建基于混合策略改进原子搜索优化最小二乘支持向量机IASO-LSSVM的快速路出口匝道风险驾驶行为分类识别模型。数值仿真实验结果表明:IASO算法在12个基准测试函数数值仿真结果的平均值、标准差、最佳适应度和最差适应度等方面均更接近最佳优化值。IASO-LSSVM模型相较于ASO-LSSVM和LSSVM等模型的风险驾驶行为分类识别结果误差指标正确率、精确率、召回率和F1值分别增加11.5~24.5、14.1~29.0、15.1~28.6和14.7~31.2个百分点,且在不同类型风险驾驶行为识别结果中误差变化范围最小。IASO算法参数寻优求解精度和收敛速率优于ASO算法,且IASO-LSSVM模型可用于不同类型风险驾驶行为精准识别,可为车辆行驶轨迹状态合理判别,制定风险驾驶行为预警防控措施提供数据支撑与理论依据。