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.

Search recommendation model based on user search behavior and gradual forgetting collaborative filtering strategy

The existing search engines are lack of the consideration of personalization and display the same search results for different users despite their differences in interesting and purpose. By analyzing user＇s dynamic search behavior, the paper introduces a new method of using a keyword query graph to express user＇s dynamic search behavior, and uses Bayesian network to construct the prior probability of keyword selection and the migration probability between keywords for each user. To reflect the dynamic changes of the user＇s preference, the paper introduces non-lineal gradual forgetting collaborative filtering strategy into the personalized search recommendation model. By calculating the similarity between each two users, the model can do the recommendation based on neighbors and be used to construct the personalized search engine.

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。

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。

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。

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。

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.

建筑工人的日常生活信息搜寻行为研究

[目的/意义]针对建筑工人开展调查,了解该群体应用智能设备的日常生活信息搜寻行为,旨在供相关信息服务部门参考,以便改善信息服务,满足他们的日常生活信息需求。[方法/过程]基于SOR模型和日常生活信息搜寻行为理论构建建筑工人日常生活信息搜寻行为分析框架,以此为基础提出六大假设。[结果/结论]现阶段建筑工人日常生活信息搜寻行为处于中等水平;社会因素是建筑工人日常生活信息搜寻行为的重要环节,具有正向影响作用;个人心理因素起完全中介作用。

从信息需求情境的视角看用户搜索行为研究

[目的/意义]对2002-2022年间我国用户搜索行为的成果进行梳理和分析,厘清我国用户搜索行为研究的发展脉络,总结研究的变化和前沿动态,分析研究的发展和趋势,为未来的用户搜索行为研究提供借鉴和参考。[方法/过程]采用内容分析法,从信息需求情境的视角将信息搜索行为研究内容分为3个大类、9个子类目,系统地分析出每个领域的研究对象、关注重点及其特点。[结果/结论]该领域研究成果颇丰,但尚有研究盲区和不足,可以形成学者们后续研究中的关注点,如研究对象相对狭窄、研究行业的选取比较固化、研究内容有待细化、研究方法有待突破。

基于行为效应的双资源柔性作业车间调度

针对具有机器和工人双资源约束的柔性作业车间,考虑了影响加工时间的各种因素,以完工时间、生产成本、车间总能耗为目标,提出了一种改进的NSGA-Ⅱ和禁忌搜索结合的多目标混合算法(MOGATS)。算法设计混合初始化方法保证初始解的质量和多样性,并在考虑运输时间的基础上进行贪婪式解码,采用自适应交叉、变异算子,在Pareto前沿中找到各个目标的最优个体进行禁忌搜索,综合两种算法提高全局和局部搜索能力。通过实例仿真,结果均优于对比算法,验证了MOGATS算法的有效性和可行性。

Seeker optimization algorithm:a novel stochastic search algorithm for global numerical optimization

A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.

搜索引擎平台优化策略影响用户福利的机理研究

既有研究普遍认为搜索引擎平台优化策略可提升用户检索效率,却较少剖析其对用户福利的作用机理。构建搜索引擎平台优化策略的两阶段理论模型,考察搜索引擎平台优化策略的作用机理和用户福利效应,从而增进搜索引擎平台优化策略的有效应用。结果显示:搜索引擎平台优化策略存在长短期效应,短期来看,搜索引擎平台优化策略的排列均衡违背用户检索意愿,虽增加搜索引擎平台利润,却损害用户福利;长期来看,搜索引擎平台优化策略的排列均衡将契合用户检索意愿,提升用户福利,并对搜索引擎平台利润产生负向影响。因此,搜索引擎平台企业可以针对非热门品牌关键词实施搜索引擎平台优化策略,吸引提供商参与投标,提升平台拍卖利润。

不同风险水平下驾驶员风险感知能力对驾驶决策的影响

目的考察驾驶员的风险感知能力(高、低)在不同交通风险水平(高、低、无)对驾驶决策的影响,以及驾驶员的视觉搜索特征。方法采用风险感知视频测试将40名年轻驾驶员分为高风险感知组与低风险感知组,向驾驶员展示具有不同风险水平的交通图片,要求驾驶员做出是否减速的决策,通过眼动仪记录驾驶决策、反应时和眼动情况。结果在低风险水平,高风险感知驾驶员的减速决策得分显著高于低风险感知驾驶员。在无风险水平,高风险感知驾驶员的总注视时间和凝视时间显著长于低风险感知驾驶员,注视次数显著多于低风险感知驾驶员。结论高风险感知驾驶员更注重对风险的搜索,驾驶决策更加谨慎。研究结果为驾驶员的驾驶安全培训与风险感知能力检验提供参考。

基于机器学习的梯度点阵材料优化设计

点阵材料具有轻质、抗冲击、高能量吸收等特性,因而在航天飞行器承载部件设计等领域有广阔应用前景.通过对点阵材料内部杆径进行合理的梯度设计,可以提高点阵材料在高速冲击载荷作用下的动态力学性能.利用仿真模拟数据,基于随机森林模型实现了梯度点阵材料的动态力学响应预测和结构参数优化.以面心立方(face center cubic,FCC)结构梯度点阵材料为研究对象,通过对杆径参数的调整实现点阵材料密度的梯度化设计.通过LS-DYNA软件计算了密度分布不同的梯度点阵材料受到冲击载荷作用时的动态力学响应,包括冲击端面与支撑端面接触应力随时间的变化曲线.基于随机森林模型,以各层胞元的相对密度为输入,实现对点阵材料端面峰值应力的预测,并基于Gini指数分析出对不同端面处峰值应力影响最大的胞元层.将网格搜索算法与训练好的随机森林对接,分别以两个端面上的峰值应力最高作为优化目标,获得点阵材料各层胞元相对密度的最优值.模型对梯度点阵材料端面峰值应力的预测误差在5%以内.数值模拟验证结果表明,优化后所得梯度点阵材料相应端面上的峰值应力高于仿真数据集内任何结构.

消费者购前信息搜索行为对制造商平台销售模式选择的影响

为研究消费者行为对制造商平台销售模式选择的影响,构建了制造商平台销售模式选择模型,将消费者购前信息搜索行为引入模型。研究发现,若不考虑消费者购前信息搜索行为,制造商只在平台抽成比例较小或较大时选择代销模式,其他时候选择分销模式;考虑消费者购前信息搜索行为后,制造商选择分销模式的区域扩大;进一步,消费者的搜索行为使制造商在同一销售模式下的利润增加。同时,搜索概率的增加会使分销模式下供应链成员的批发和零售价格下降,而使代销模式下的价格呈非单调变化。

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.

基于鸟群搜索行为和余弦变异的改进白鲨优化算法

白鲨优化算法是受白鲨捕猎行为的启发设计的一种新元启发式算法。该算法在求解高维优化问题时,易进入早熟状态,寻优结果精度较低。为此,文章提出一种改进的白鲨优化(improved white shake optimizer,IWSO)算法。首先使用Sinusoidal混沌映射初始化种群,以提高种群多样性及初始解在解空间的分布性;其次,引入鸟群搜索行为,赋予白鲨游动速度自适应动态惯性权重,以提高算法的收敛速度;最后,在位置更新阶段引入精英白鲨余弦变异策略,利用余弦函数的周期性特征,驱使白鲨个体在精英白鲨的有限邻域内进行精细化开发,以提高收敛精度。在23个著名基准函数和CEC2014函数上做了性能对比实验,其结果表明,IWSO算法优于6种对比算法,适合求解函数优化问题。

A Data-Driven Research of Sales and Purchases on JD.com Platform

Unlike consumers in the mall or supermarkets, online consumers are “intangible” and their purchasing behaviors are affected by multiple factors, including product pricing, promotion and discounts, quality of products and brands, and the platforms where they search for the product. In this research, I study the relationship between product sales and consumer characteristics, the relationship between product sales and product qualities, demand curve analysis, and the search friction effect for different platforms. I utilized data from a randomized field experiment involving more than 400 thousand customers and 30 thousand products on JD.com, one of the world’s largest online retailing platforms. There are two focuses of the research: 1) how different consumer characteristics affect sales;2) how to set price and possible search friction for different channels. I find that JD plus membership, education level and age have no significant relationship with product sales, and higher user level leads to higher sales. Sales are highly skewed, with very high numbers of products sold making up only a small percentage of the total. Consumers living in more industrialized cities have more purchasing power. Women and singles lead to higher spending. Also, the better the product performs, the more it sells. Moderate pricing can increase product sales. Based on the research results of search volume in different channels, it is suggested that it is better to focus on app sales. By knowing the results, producers can adjust target consumers for different products and do target advertisements in order to maximize the sales. Also, an appropriate price for a product is also crucial to a seller. By the way, knowing the search friction of different channels can help producers to rearrange platform layout so that search friction can be reduced and more potential deals may be made.

基于混合量子麻雀算法的过热汽温模型参数辨识

建立高精度的过热汽温模型便于控制器的设计和参数整定,从而提升汽温的控制品质。首先针对传统的麻雀优化算法在闭环数据驱动辨识中容易陷入局部最优,收敛速度慢的问题,提出了一种混合量子行为的麻雀优化算法(QSSA)。该算法在基本麻雀算法迭代一次后采用量子策略对劣势群体进行变异,加强空间搜索能力,提高辨识精度;对于较优势群体引入Lévy随机游走策略,增加种群多样性,克服量子策略迭代后期种群性减少,易陷入局部最优的问题。其次对比多种优化算法在基准函数测试中的优化结果,验证了所提算法的优越性。最后运用某600 MW超临界机组过热汽温的现场运行数据进行模型参数的QSSA辨识,表明了所提算法的有效性,为现场过热汽温控制系统的进一步优化提供了新的途径。