Leveraging exploratory search with personality traits and interactional context

Exploratory search is a type of information seeking used by searchers who are either unfamiliar with the domain of their goal, are unsure about the ways to achieve their goals or uncertain about their goals in the first place. We present a method that utilizes interactional context and personality information in order to proactively prompt users to undertake actions for improving exploratory search and its outcome. Our approach is based on inferring exploration patterns based on the logged past behavior of users in order to produce models of behavior, which in turn are used to predict the next action in the current context. The user is classified into specific groups of users that share personality traits for which we have analyzed their search behaviors. At the same time, we assume that the users who belong within the same group show similar exploration tactics to reach their goal such as the sequence of actions performed. Having the models, we show how we can predict the next interaction of the user given a specific sequence of actions of the current session. In this way, we assist users in their exploration process and act proactively by providing meaningful recommendations and prompts towards possibly undiscovered facets of the topic under investigation.     

Investigating the role of in-situ user expectations in Web search

Pre-adoption expectations often serve as an implicit reference point in users’ evaluation of information systems and are closely associated with their goals of interactions, behaviors, and overall satisfaction. Despite the empirically confirmed impacts, users’ search expectations and their connections to tasks, users, search experiences, and behaviors have been scarcely studied in the context of online information search. To address the gap, we collected 116 sessions from 60 participants in a controlled-lab Web search study and gathered direct feedback on their in-situ expected information gains (e.g., number of useful pages) and expected search efforts (e.g., clicks and dwell time) under each query during search sessions. Our study aims to examine (1) how users’ pre-search experience, task characteristics, and in-session experience affect their current expectations and (2) how user expectations are correlated with search behaviors and satisfaction. Our results with both quantitative and qualitative evidence demonstrate that: (1) user expectation is significantly affected by task characteristics, previous and in-situ search experience; (2) user expectation is closely associated with users’ browsing behaviors and search satisfaction. The knowledge learned about user expectation advances our understanding of users’ search behavioral patterns and their evaluations of interaction experience and will also facilitate the design, implementation, and evaluation of expectation-aware user models, metrics, and information retrieval (IR) systems.     

学术信息搜寻过程中的用户信息焦虑研究

【目的/意义】探索学术信息搜寻过程中用户信息焦虑的动态变化过程,了解其成因,为减轻用户在学术信 息搜寻中的信息焦虑程度和构建良好学术信息环境提供建议。【方法/过程】采用半结构化访谈和问卷调查的方法, 通过文献调研和8份访谈数据修订学术搜索信息焦虑量表,根据信息搜寻过程理论进行阶段划分并编制问卷,对 649名学术用户进行调查。【结果/结论】研究发现信息质量焦虑、信息数量焦虑、信息检索工具焦虑、信息素养焦虑、 信息获取焦虑及信息处理焦虑是学术信息搜寻中普遍存在的6种焦虑类型,其中学术信息获取是引发焦虑的主要 类型;学术信息搜寻过程中信息焦虑呈先增后减的变化模式,收集阶段用户焦虑程度最高;每个阶段出现的信息焦 虑类型和程度存在明显差异,初始阶段、选择阶段和表述阶段信息焦虑出现类型较为集中和单一,探索阶段用户产 生的信息焦虑类型最为复杂。【创新/局限】构建学术信息搜寻各阶段行为框架,从微观层面将信息焦虑的研究细化 到学术搜索的各个阶段。     

基于元数据关注的学术文献搜索结果选判行为研究 ——以知网平台为例

【目的/意义】探究学术文献搜索结果选判过程中用户元数据关注特点及其与选择行为的关系,为检索系统 优化、用户选择行为指导提供理论依据。【目的/意义】利用出声思维法、半结构化访谈以及关键事件法对30名在读 硕士和博士研究生对知网学术文献检索结果的选判行为进行观察与访谈,对得到的材料进行分析。【结果/结论】用 户对题名、期刊、摘要等元数据信息关注较多,而对相似文献、下载量等元数据信息关注较少;高年级用户对于文献 内容特征的关注程度明显高于低年级用户,但对于新功能的关注度较低;用户关注的元数据信息越多,检索的次数 也越多;用户常用的元数据关注路径为LB、LB-LD和SI-LB,文献选择效果较好的路径为LB-LD和SI-LB。【创新/ 局限】充实丰富了学术信息搜索结果选判行为研究的视角,以期为后续的检索系统优化、用户选判行为指导提供理 论支持;本次研究得到的结论较适用于初级科研用户,对于经验丰富的科研用户适用性仍有待考证。     

Extending lead user theory to users’ innovation-related knowledge sharing in the online user community: The mediating roles of social capital and perceived behavioral control

The research on users as a source of innovation has been coming into blossom and the studies about the effect of users’ lead userness on their innovation-related activities are drawing more and more attention from both academic and business circles. However, there have been few empirical studies exploring the relationship between users’ lead userness and their innovation-related knowledge sharing behavior in the context of online user community and the mediating effects of users’ social capital and their perceived behavioral control on this relationship. By empirically analyzing the 140 data collected from an online user community that is used as an important source of innovation for a company with the structural equation modeling analysis through the partial least squares method, this study reveals that users’ lead userness has a positive relationship with their innovation-related knowledge sharing in the online user community and users’ social capital and perceived behavioral control jointly and fully mediate this positive relationship. Based on the new findings, this study is expected to provide useful implications which can contribute to widening and deepening the research stream about the effect of users’ lead userness on their innovation-related knowledge sharing in the online user community.     

Implicit information need as explicit problems,help, and behavioral signals

Information need is one of the most fundamental aspects of information seeking, which traditionally conceptualizes as the initiation phase of an individual’s information seeking behavior. However, the very elusive and inexpressible nature of information need makes it hard to elicit from the information seeker or to extract through an automated process. One approach to understanding how a person realizes and expresses information need is to observe their seeking behaviors, to engage processes with information retrieval systems, and to focus on situated performative actions. Using Dervin’s Sense-Making theory and conceptualization of information need based on existing studies, the work reported here tries to understand and explore the concept of information need from a fresh methodological perspective by examining users’ perceived barriers and desired helps in different stages of information search episodes through the analyses of various implicit and explicit user search behaviors. In a controlled lab study, each participant performed three simulated online information search tasks. Participants’ implicit behaviors were collected through search logs, and explicit feedback was elicited through pre-task and post-task questionnaires. A total of 208 query segments were logged, along with users’ annotations on perceived problems and help. Data collected from the study was analyzed by applying both quantitative and qualitative methods. The findings identified several behaviors – such as the number of bookmarks, query length, number of the unique queries, time spent on search results observed in the previous segment, the current segment, and throughout the session – strongly associated with participants’ perceived barriers and help needed. The findings also showed that it is possible to build accurate predictive models to infer perceived problems of articulation of queries, useless and irrelevant information, and unavailability of information from users’ previous segment, current segment, and whole session behaviors. The findings also demonstrated that by combining perceived problem(s) and search behavioral features, it was possible to infer users’ needed help(s) in search with a certain level of accuracy (78%).     

Exploring the immediate and short-term effects of peer advice and cognitive authority on Web search behavior

An individual's Web search behavior can be influenced by a number of factors, including features and functions of a search engine as well as search education. In contrast to the long-lasting attention to the algorithm and interface dimensions of search, there is a lack of research concerned with the potential effects of user education on search behavior. To address this gap, we ran a three-session field-lab-combined study to examine the effects of user education from two distinct sources – peer advice and cognitive authority (operationalized as video-based student's advice and expert's advice respectively) – on Web search behavior in two different search task scenarios (i.e., factual specific and factual amorphous tasks). We also tested if these behavioral effects persist for a short period of time when the explicit search tips are removed. Using 185 task session data generated by 31 participants in two field and one lab sessions, this study demonstrates that: (1) both peer advice and cognitive authority are effective in stimulating immediate behavioral changes in Web search; (2) the immediate behavioral impact of search advice is broader in factual amorphous task than in factual specific task; (3) framing search tips as the advice from cognitive authority is more likely to generate continuing, short-term effects on Web search behaviors. This research has implications for the design of task-aware user education as well as the study of users’ interactions with IR systems in general.     

Analysis of multiple query reformulations on the web: The interactive information retrieval context

This study examines the facets and patterns of multiple Web query reformulations with a focus on reformulation sequences. Based on IR interaction models, it was presumed that query reformulation is the product of the interaction between the user and the IR system. Query reformulation also reflects the interplay between the surface and deeper levels of user interaction. Query logs were collected from a Web search engine through the selection of search sessions in which users submitted six or more unique queries per session. The final data set was composed of 313 search sessions. Three facets of query reformulation (content, format, and resource) as well as nine sub-facets were derived from the data. In addition, analysis of modification sequences identified eight distinct patterns: specified, generalized, parallel, building-block, dynamic, multitasking, recurrent, and format reformulation. Adapting Saracevic’s stratified model, the authors develop a model of Web query reformulation based on the results of the study. The implications for Web search engine design are finally discussed and the functions of an interactive reformulation tool are suggested.     

Automatic suggestion of phrasal-concept queries for literature search

Both general and domain-specific search engines have adopted query suggestion techniques to help users formulate effective queries. In the specific domain of literature search (e.g., finding academic papers), the initial queries are usually based on a draft paper or abstract, rather than short lists of keywords. In this paper, we investigate phrasal-concept query suggestions for literature search. These suggestions explicitly specify important phrasal concepts related to an initial detailed query. The merits of phrasal-concept query suggestions for this domain are their readability and retrieval effectiveness: (1) phrasal concepts are natural for academic authors because of their frequent use of terminology and subject-specific phrases and (2) academic papers describe their key ideas via these subject-specific phrases, and thus phrasal concepts can be used effectively to find those papers. We propose a novel phrasal-concept query suggestion technique that generates queries by identifying key phrasal-concepts from pseudo-labeled documents and combines them with related phrases. Our proposed technique is evaluated in terms of both user preference and retrieval effectiveness. We conduct user experiments to verify a preference for our approach, in comparison to baseline query suggestion methods, and demonstrate the effectiveness of the technique with retrieval experiments.     

不同网络平台用户情感表达特征对比分析

[目的/意义]网络用户主要通过购物类、社交类和点评类三种常见网络类型平台发表带有情感倾向的评论,但是由于用户群体、可评论时间、可评论次数以及评论方式等的不同,使得这三种网站类型中的用户对于同一主题的情感表达存在较大差异。文章从用户情感表达特征角度对比评测购物类、社交类和点评类三种主流网络平台中用户情感评论特点,为情感分析数据源选择提供借鉴。[方法/过程]在情感分析的基础上,通过对评论特点、用户情感特征和用户痛点等的对比分析和实证分析,探索三种平台的情感表达特点。[结果/结论]实验结果表明,不同类型网络平台评论的主要内容、情感特征以及痛点表现方面均存在明显差异,进而对情感分析信息源选择提供依据。     

基于用户认知框架的商品知识融合实证研究

[目的/意义]旨在改善在线商品信息描述,降低用户认知负担,提升在线购物效率。采用框架描述法构建用户认知框架,在此基础上实现商品知识融合,以期解决在线商品信息的冲突矛盾和冗余问题。[方法/过程]采集数码相机在线评论信息,以框架法进行用户认知框架表示,利用规则融合方法构建商品知识融合模型,进行了用户认知框架的融合模型验证和效果分析。[结果/结论]知识融合能解决商品信息冗余问题,能填补空值型商品知识信息,减少电商平台商品信息描述流量成本,帮助用户理解商品知识矛盾问题,提升电商平台商品知识描述和呈现质量。     

The interaction effects of online reviews and free samples on consumers’ downloads: An empirical analysis

Consumers’ software purchase decisions are influenced both by online reviews and by their experiences with free samples provided by firms. This paper empirically investigates the differential effects of online reviews (user and editor ratings) on consumers’ sample downloading behavior, using a dataset drawn from a large software free sampling website CNET.com. Our findings extend the previous research by suggesting that information disclosure levels of free samples (indicated by licenses) moderates the impacts of online reviews on consumers’ sample downloads. For samples that disclose a great level of information, higher user ratings can increase downloads; otherwise, higher user ratings fail to increase downloads. When both user and editor ratings are available to consumers, only user ratings can increase sample downloads. The findings can be explained by consumers’ two-stage information process whereby consumers first refer to online reviews and then determine whether to sample software. This study provides practical implications on the design of information disclosure channel and offers suggestions for firms regarding how to select and apply sample licenses.     

Asking Clarifying Questions: To benefit or to disturb users in Web search?

Modern information-seeking systems are becoming more interactive, mainly through asking Clarifying Questions (CQs) to refine users’ information needs. System-generated CQs may be of different qualities. However, the impact of asking multiple CQs of different qualities in a search session remains underexplored. Given the multi-turn nature of conversational information-seeking sessions, it is critical to understand and measure the impact of CQs of different qualities, when they are posed in various orders. In this paper, we conduct a user study on CQ quality trajectories, i.e., asking CQs of different qualities in chronological order. We aim to investigate to what extent the trajectory of CQs of different qualities affects user search behavior and satisfaction, on both query-level and session-level. Our user study is conducted with 89 participants as search engine users. Participants are asked to complete a set of Web search tasks. We find that the trajectory of CQs does affect the way users interact with Search Engine Result Pages (SERPs), e.g., a preceding high-quality CQ prompts the depth users to interact with SERPs, while a preceding low-quality CQ prevents such interaction. Our study also demonstrates that asking follow-up high-quality CQs improves the low search performance and user satisfaction caused by earlier low-quality CQs. In addition, only showing high-quality CQs while hiding other CQs receives better gains with less effort. That is, always showing all CQs may be risky and low-quality CQs do disturb users. Based on observations from our user study, we further propose a transformer-based model to predict which CQs to ask, to avoid disturbing users. In short, our study provides insights into the effects of trajectory of asking CQs, and our results will be helpful in designing more effective and enjoyable search clarification systems.     

Real time search on the web: Queries,topics, and economic value

Real time search is an increasingly important area of information seeking on the Web. In this research, we analyze 1,005,296 user interactions with a real time search engine over a 190 day period. Using query log analysis, we investigate searching behavior, categorize search topics, and measure the economic value of this real time search stream. We examine aggregate usage of the search engine, including number of users, queries, and terms. We then classify queries into subject categories using the Google Directory topical hierarchy. We next estimate the economic value of the real time search traffic using the Google AdWords keyword advertising platform. Results shows that 30% of the queries were unique (used only once in the entire dataset), which is low compared to traditional Web searching. Also, 60% of the search traffic comes from the search engine’s application program interface, indicating that real time search is heavily leveraged by other applications. There are many repeated queries over time via these application program interfaces, perhaps indicating both long term interest in a topic and the polling nature of real time queries. Concerning search topics, the most used terms dealt with technology, entertainment, and politics, reflecting both the temporal nature of the queries and, perhaps, an early adopter user-based. However, 36% of the queries indicate some geographical affinity, pointing to a location-based aspect to real time search. In terms of economic value, we calculate this real time search stream to be worth approximately US $33,000,000 (US $33 M) on the online advertising market at the time of the study. We discuss the implications for search engines and content providers as real time content increasingly enters the main stream as an information source.     

A study of awareness in multimedia search

Awareness of another’s activity is an important aspect of facilitating collaboration between users, enabling an “understanding of the activities of others” (Dourish & Bellotti, 1992). In this paper we investigate the role of awareness and its effect on search performance and behaviour in collaborative multimedia retrieval. We focus on the scenario where two users are searching at the same time on the same task, and via an interface, can see the activity of the other user. The main research question asks: does awareness of another searcher aid a user when carrying out a multimedia search session?To encourage awareness, an experimental study was designed where two users were asked to compete to find as many relevant video shots as possible under different awareness conditions. These were individual search (no awareness), Mutual awareness (where both users could see the other’s search screen), and unbalanced awareness (where one user is able to see the other’s screen, but not vice-versa). Twelve pairs of users were recruited, and the four worst performing TRECVID 2006 search topics were used as search tasks, under four different awareness conditions. We present the results of this study, followed by a discussion of the implications for multimedia information retrieval systems.     

基于用户评论分析的网络学术信息资源评价体系模型研究

[目的/意义]从用户角度,通过用户评价内容挖掘构建形成网络学术信息资源评价模型,为网络学术信息资源评价提供一个新的视角,并为其更进一步深入研究奠定基础。[方法/过程]文章在利用爬虫工作自动抓取三个知名学术网站用户评论的语料库基础上,运用数据挖掘研究方法对评论数据进行分词、聚类,根据词间与词对关系,构建形成评价体系模型。[结果/结论]基于用户评论挖掘构建形成涵盖资源内容属性、资源外部特征、网络功能属性、获取过程、用户体验五个维度的网络学术信息资源评价体系模型。该体系模型反映出科学用户在利用新兴网络学术信息资源过程中对资源自身属性和平台规范性的关切,是用户与利用正式学术信息资源的本质区别。     

Adaptive visualization for exploratory information retrieval

As the volume and breadth of online information is rapidly increasing, ad hoc search systems become less and less efficient to answer information needs of modern users. To support the growing complexity of search tasks, researchers in the field of information developed and explored a range of approaches that extend the traditional ad hoc retrieval paradigm. Among these approaches, personalized search systems and exploratory search systems attracted many followers. Personalized search explored the power of artificial intelligence techniques to provide tailored search results according to different user interests, contexts, and tasks. In contrast, exploratory search capitalized on the power of human intelligence by providing users with more powerful interfaces to support the search process. As these approaches are not contradictory, we believe that they can re-enforce each other. We argue that the effectiveness of personalized search systems may be increased by allowing users to interact with the system and learn/investigate the problem in order to reach the final goal. We also suggest that an interactive visualization approach could offer a good ground to combine the strong sides of personalized and exploratory search approaches. This paper proposes a specific way to integrate interactive visualization and personalized search and introduces an adaptive visualization based search system Adaptive VIBE that implements it. We tested the effectiveness of Adaptive VIBE and investigated its strengths and weaknesses by conducting a full-scale user study. The results show that Adaptive VIBE can improve the precision and the productivity of the personalized search system while helping users to discover more diverse sets of information.     

The influence of task and gender on search and evaluation behavior using Google

To improve search engine effectiveness, we have observed an increased interest in gathering additional feedback about users’ information needs that goes beyond the queries they type in. Adaptive search engines use explicit and implicit feedback indicators to model users or search tasks. In order to create appropriate models, it is essential to understand how users interact with search engines, including the determining factors of their actions. Using eye tracking, we extend this understanding by analyzing the sequences and patterns with which users evaluate query result returned to them when using Google. We find that the query result abstracts are viewed in the order of their ranking in only about one fifth of the cases, and only an average of about three abstracts per result page are viewed at all. We also compare search behavior variability with respect to different classes of users and different classes of search tasks to reveal whether user models or task models may be greater predictors of behavior. We discover that gender and task significantly influence different kinds of search behaviors discussed here. The results are suggestive of improvements to query-based search interface designs with respect to both their use of space and workflow.     

推荐时机对个性化推荐采纳影响的实验研究

[目的/意义]推荐时机是影响用户采纳的重要因素,如何选择最佳推荐时机,具有实践意义。[方法/过程]首先对推荐时机进行更为合理的界定;其次利用E-prime软件实现商品推荐的在线购物情景实验,基于行为、认知和主观调查实验数据,从行为和认知层面揭示了不同推荐时机对用户采纳影响的客观规律。[结果/结论]从推荐时机的视角为电商企业优化个性化推荐提供了有益建议。     

基于动态用户画像的学术虚拟社区粘性驱动机制研究

[目的/意义]基于动态用户画像探索学术虚拟社区的粘性驱动机制在于用户角色精准定位，有助于提升用户忠诚度、信任度、留存率、回访率。[方法/过程]依据社区属性和用户感知分析学术虚拟社区的粘性驱动因子；以用户自然属性、行为属性、心理特征为用户画像的数据来源，建构包括基础数据、行为建模、服务应用、评价反馈4个模块的动态用户画像结构模型；结合驱动因子与结构模型构建了粘性驱动机制模型。[结果/结论]模型深度刻画了学术虚拟社区用户全貌，为优化系统效能和精准化服务指供指导，以期增强学术信息资源流转与学术影响力。