On domain expertise-based roles in collaborative information retrieval

Collaborative information retrieval involves retrieval settings in which a group of users collaborates to satisfy the same underlying need. One core issue of collaborative IR models involves either supporting collaboration with adapted tools or developing IR models for a multiple-user context and providing a ranked list of documents adapted for each collaborator. In this paper, we introduce the first document-ranking model supporting collaboration between two users characterized by roles relying on different domain expertise levels. Specifically, we propose a two-step ranking model: we first compute a document-relevance score, taking into consideration domain expertise-based roles. We introduce specificity and novelty factors into language-model smoothing, and then we assign, via an Expectation–Maximization algorithm, documents to the best-suited collaborator. Our experiments employ a simulation-based framework of collaborative information retrieval and show the significant effectiveness of our model at different search levels.     

A model for understanding collaborative information behavior in context: A study of two healthcare teams

Collaborative information behavior is an essential aspect of organizational work; however, we have very limited understanding of this behavior. Most models of information behavior focus on the individual seeker of information. In this paper, we report the results from two empirical studies that investigate aspects of collaborative information behavior in organizational settings. From these studies, we found that collaborative information behavior differs from individual information behavior with respect to how individuals interact with each other, the complexity of the information need, and the role of information technology. There are specific triggers for transitioning from individual to collaborative information behavior, including lack of domain expertise. The information retrieval technologies used affect collaborative information behavior by acting as important supporting mechanisms. From these results and prior work, we develop a model of collaborative information behavior along the axes of participant behavior, situational elements, and contextual triggers. We also present characteristics of collaborative information system including search, chat, and sharing. We discuss implications for the design of collaborative information retrieval systems and directions for future work.     

Evaluating collaborative information-seeking interfaces with a search-oriented inspection method and re-framed information seeking theory

Despite the many implicit references to the social contexts of search within information seeking and retrieval research, there has been relatively little work that has specifically investigated the additional requirements for collaborative information-seeking interfaces. Here, we re-assess an existing analytical inspection framework, designed for individual information seeking, and then apply it to evaluate a recent collaborative information-seeking interface: SearchTogether. The framework was built upon two models of solitary information seeking, and so as part of the re-assessment we first re-frame the models for collaborative contexts. We re-frame a model of search tactics, providing revised definitions that consider known collaborators. We then re-frame a model of searcher profiles to analyse support for different group dynamics. After presenting an analysis of SearchTogether, we reflect on its accuracy, showing that the framework identified eight known truths, eight new insights, and no known-to-be-untrue insights into the design. We conclude that the framework: (a) can still be applied to collaborative information-seeking interfaces; (b) can successfully produce additional requirements for collaborative information-seeking interfaces; and (c) can successfully model different dynamics of collaborating searchers.     

MineRank: Leveraging users’ latent roles for unsupervised collaborative information retrieval

Research on collaborative information retrieval (CIR) has shown positive impacts of collaboration on retrieval effectiveness in the case of complex and/or exploratory tasks. The synergic effect of accomplishing something greater than the sum of its individual components is reached through the gathering of collaborators’ complementary skills. However, these approaches often lack the consideration that collaborators might refine their skills and actions throughout the search session, and that a flexible system mediation guided by collaborators’ behaviors should dynamically adapt to this situation in order to optimize search effectiveness. In this article, we propose a new unsupervised collaborative ranking algorithm which leverages collaborators’ actions for (1) mining their latent roles in order to extract their complementary search behaviors; and (2) ranking documents with respect to the latent role of collaborators. Experiments using two user studies with respectively 25 and 10 pairs of collaborators demonstrate the benefit of such an unsupervised method driven by collaborators’ behaviors throughout the search session. Also, a qualitative analysis of the identified latent role is proposed to explain an over-learning noticed in one of the datasets.     

Search on surfaces: Exploring the potential of interactive tabletops for collaborative search tasks

Collaborative information seeking often takes place in co-located settings; such opportunities may be planned (business colleagues meeting in a conference room or students working together in a library) or spontaneous (family members gathered in their living room or friends meeting at a café). Surface computing technologies (i.e., interactive tabletops) hold great potential for enhancing collaborative information seeking activities. Such devices provide engaging direct manipulation interactions, facilitate awareness of collaborators’ activities, and afford spatial organization of content. However, current tabletop technologies also present several challenges that creators of collaborative information seeking system must account for in their designs. In this article, we explore the design space for collaborative search systems on interactive tabletops, discussing the benefits and challenges of creating search applications for these devices. We discuss how features of our tabletop search prototypes TeamSearch, FourBySix Search, Cambiera, and WeSearch, illustrate different aspects of this design space.     

Social summarization in collaborative web search

A critical challenge for Web search engines concerns how they present relevant results to searchers. The traditional approach is to produce a ranked list of results with title and summary (snippet) information, and these snippets are usually chosen based on the current query. Snippets play a vital sensemaking role, helping searchers to efficiently make sense of a collection of search results, as well as determine the likely relevance of individual results. Recently researchers have begun to explore how snippets might also be adapted based on searcher preferences as a way to better highlight relevant results to the searcher. In this paper we focus on the role of snippets in collaborative web search and describe a technique for summarizing search results that harnesses the collaborative search behaviour of communities of like-minded searchers to produce snippets that are more focused on the preferences of the searchers. We go on to show how this so-called social summarization technique can generate summaries that are significantly better adapted to searcher preferences and describe a novel personalized search interface that combines result recommendation with social summarization.     

CIRLab: A groupware framework for collaborative information retrieval research

In this paper we describe the design of a groupware framework, CIRLab, for experimenting with collaborative information retrieval (CIR) techniques in different search scenarios. This framework has been designed applying design patterns and an object-oriented middleware platform to maximize its reusability and adaptability in new contexts with a minimum of programming efforts. Our collaborative search application comprises three main modules: the Core, which supports various modern state-of-the-art CIR techniques that can be reused or extended in a distributed collaborative environment; the Facades Mediator, an event-driven notification service which allows easy integration between the Core and front-end applications; and finally, the Actions Tracker, which allows researchers to perform experiments on the different elements involved in the collaborative search sessions. The applying of this framework is illustrated through the analysis of the collaborative search-driven development case study.     

Let’s search together,but not too close! An analysis of communication and performance in collaborative information seeking

Communication is considered to be one of the most essential components of collaboration, but our understanding as to which form of communication provides the most optimal cost-benefit balance lacks severely. To help investigate effects of various communication channels on a collaborative project, we conducted a user study with 30 pairs (60 participants) in three different conditions – co-located, remotely located with text chat, and remotely located with text as well as audio chat, in an exploratory search task. Using both quantitative and qualitative data analysis, we found that teams with remotely located participants were more effective in terms of being able to explore more diverse information. Adding audio support for remote collaboration helped participants to lower their cognitive load as well as negative emotions compared to those working in the same space. We also show how these findings could help design more effective systems for collaborative information seeking tasks using adequate and appropriate communication. We argue that collaboration is an important aspect of human-centered IR, and that our work provides interesting insights into people doing information seeking/retrieval in collaboration.     

Querytogether: Enabling entity-centric exploration in multi-device collaborative search

Collaborative and co-located information access is becoming increasingly common. However, fairly little attention has been devoted to the design of ubiquitous computing approaches for spontaneous exploration of large information spaces enabling co-located collaboration. We investigate whether an entity-based user interface provides a solution to support co-located search on heterogeneous devices. We present the design and implementation of QueryTogether, a multi-device collaborative search tool through which entities such as people, documents, and keywords can be used to compose queries that can be shared to a public screen or specific users with easy touch enabled interaction. We conducted mixed-methods user experiments with twenty seven participants (nine groups of three people), to compare the collaborative search with QueryTogether to a baseline adopting established search and collaboration interfaces. Results show that QueryTogether led to more balanced contribution and search engagement. While the overall s-recall in search was similar, in the QueryTogether condition participants found most of the relevant results earlier in the tasks, and for more than half of the queries avoided text entry by manipulating recommended entities. The video analysis demonstrated a more consistent common ground through increased attention to the common screen, and more transitions between collaboration styles. Therefore, this provided a better fit for the spontaneity of ubiquitous scenarios. QueryTogether and the corresponding study demonstrate the importance of entity based interfaces to improve collaboration by facilitating balanced participation, flexibility of collaboration styles and social processing of search entities across conversation and devices. The findings promote a vision of collaborative search support in spontaneous and ubiquitous multi-device settings, and better linking of conversation objects to searchable entities.     

Collaborative Information Retrieval in an information-intensive domain

In this article we investigate the expressions of collaborative activities within information seeking and retrieval processes (IS&R). Generally, information seeking and retrieval is regarded as an individual and isolated process in IR research. We assume that an IS&R situation is not merely an individual effort, but inherently involves various collaborative activities. We present empirical results from a real-life and information-intensive setting within the patent domain, showing that the patent task performance process involves highly collaborative aspects throughout the stages of the information seeking and retrieval process. Furthermore, we show that these activities may be categorised and related to different stages in an information seeking and retrieval process. Therefore, the assumption that information retrieval performance is purely individual needs to be reconsidered. Finally, we also propose a refined IR framework involving collaborative aspects.     

Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy

In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy.     

A probabilistic music recommender considering user opinions and audio features

A recommender system has an obvious appeal in an environment where the amount of on-line information vastly outstrips any individual’s capability to survey. Music recommendation is considered a popular application area. In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focus on capturing precise similarities among users or items based on user historical ratings. Despite the valuable information from audio features of music itself, however, few studies have investigated how to utilize information extracted directly from music for personalized recommendation in CMRS. In this paper, we describe a CMRS based on our proposed item-based probabilistic model, where items are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. In addition, this model has been extended for improved recommendation performance by utilizing audio features that help alleviate three well-known problems associated with data sparseness in collaborative recommender systems: user bias, non-association, and cold start problems in capturing accurate similarities among items. Experimental results based on two real-world data sets lead us to believe that content information is crucial in achieving better personalized recommendation beyond user ratings. We further show how primitive audio features can be combined into aggregate features for the proposed CRMS and analyze their influences on recommendation performance. Although this model was developed originally for music collaborative recommendation based on audio features, our experiment with the movie data set demonstrates that it can be applied to other domains.     

在线旅游信息协作搜索模式探讨

由于在线环境的复杂性和旅游信息的特殊性,游客需要协作搜索信息进行旅游规划。根据协作信息搜索在一些领域的研究成果,结合旅游信息的特殊性、以及支持在线旅游信息协作搜索的技术,设计了新的在线旅游信息协作搜索模型,并对在线旅游信息协作搜索过程和模式进行探讨。     

基于共享心智模型的在线旅游信息协作搜索团队认知研究

[目的/意义] 深化对在线旅游信息协作搜索的整体认识,为专业化旅游信息协作搜索服务平台建设优化等提供启发和借鉴。[方法/过程] 聚焦于在线旅游信息协作搜索情境下的团队认知,以共享心智模型表征团队认知,以高校学生为研究样本,应用扎根理论方法,探讨在线旅游信息协作搜索团队共享心智模型建构过程。[结果/结论] 识别出四大核心环节——个体感知与探索、团队任务解析、团队信息收集和团队信息决策,并理清了各个环节的认知活动与共享心智模型内容。     

Division of labour and sharing of knowledge for synchronous collaborative information retrieval

Synchronous collaborative information retrieval (SCIR) is concerned with supporting two or more users who search together at the same time in order to satisfy a shared information need. SCIR systems represent a paradigmatic shift in the way we view information retrieval, moving from an individual to a group process and as such the development of novel IR techniques is needed to support this. In this article we present what we believe are two key concepts for the development of effective SCIR namely division of labour (DoL) and sharing of knowledge (SoK). Together these concepts enable coordinated SCIR such that redundancy across group members is reduced whilst enabling each group member to benefit from the discoveries of their collaborators. In this article we outline techniques from state-of-the-art SCIR systems which support these two concepts, primarily through the provision of awareness widgets. We then outline some of our own work into system-mediated techniques for division of labour and sharing of knowledge in SCIR. Finally we conclude with a discussion on some possible future trends for these two coordination techniques.     

Information security management needs more holistic approach: A literature review

Information technology has dramatically increased online business opportunities; however these opportunities have also created serious risks in relation to information security. Previously, information security issues were studied in a technological context, but growing security needs have extended researchers' attention to explore the management role in information security management. Various studies have explored different management roles and activities, but none has given a comprehensive picture of these roles and activities to manage information security effectively. So it is necessary to accumulate knowledge about various managerial roles and activities from literature to enable managers to adopt these for a more holistic approach to information security management. In this paper, using a systematic literature review approach, we synthesised literature related to management's roles in information security to explore specific managerial activities to enhance information security management. We found that numerous activities of management, particularly development and execution of information security policy, awareness, compliance training, development of effective enterprise information architecture, IT infrastructure management, business and IT alignment and human resources management, had a significant impact on the quality of management of information security. Thus, this research makes a novel contribution by arguing that a more holistic approach to information security is needed and we suggest the ways in which managers can play an effective role in information security. This research also opens up many new avenues for further research in this area.     

协同创新网络演化模型及仿真研究——基于类DNA翻译过程

在综合协同创新网络基本属性和独有特质的基础上,通过刻画外界刺激转变过程和网络异质主体行为,构建出以异质性主体为网络要素、以刺激-反应机制为演化机理的协同创新网络类DNA翻译过程模型。运用系统仿真方法,分析了从协同创新网络的拓扑结构变动规律,知识元素和网络拓扑结构的耦合演化关系,网络异质主体的角色作用及主体开放程度与知识流动效率的关系等多个角度渐次深入地分析了协同创新网络演化过程中网络结构变动特点及其与知识元素、主体要素间深层次地交互作用关系。结果表明：协同创新网络发展过程中会多次涌现小世界特性,但程度、原因不尽相同;技术知识和网络结构存在协同演化现象;强势企业始终是协同创新网络的主导;主体开放程度对技术知识流动效率提升幅度有很大影响。强化企业在技术创新中的主体地位,并增强异质性主体间的开放程度,是提高协同创新绩效的有力保障。     

群体智慧视域下智慧政府信息协同满意度感知范畴研究

【目的/意义】在建设“让人民满意的服务型政府”思想指引下,获取智慧群体对智慧政府信息协同满意度 的感知范畴,以期为构建让人民满意的智慧政府信息协同网络结构提供决策支持。【方法/过程】首先,界定智慧群 体的概念与析出满意度产生的生理机制;其次,采用深度访谈的方法,对符合标准的智慧个体围绕智慧政府信息协 同满意度感知这一研究主题进行深度访谈,获取访谈数据;最后,采用扎根理论方法对访谈数据进行分析,析出智 慧政府信息协同满意度感知范畴。【结果/结论】研究结果表明：智慧政府信息协同满意度感知范畴共包含信息协 同需求端感知因子、信息协同供给端感知因子、信息协同服务载体感知因子、信息协同环境感知因子4个感知因子, 其中信息协同需求端感知因子和信息协同供给端感知因子更易影响政府公务员群体对智慧政府信息协同满意度 的感知。【创新/局限】本研究不足之处在于,由于篇幅有限,本文尚未实现基于智慧政府信息协同满意度感知的指 引,对融合满意度感知的智慧政府信息协同网络结构模型的构建进行研究。     

Design and evaluation of awareness mechanisms in CiteSeer

Awareness has been extensively studied in human computer interaction (HCI) and computer supported cooperative work (CSCW). The success of many collaborative systems hinges on effectively supporting awareness of different collaborators, their actions, and the process of creating shared work products. As digital libraries are increasingly becoming more than just repositories for information search and retrieval – essentially fostering collaboration among its community of users – awareness remains an unexplored research area in this domain. We are investigating awareness mechanisms in CiteSeer, a scholarly digital library for the computer and information science domain. CiteSeer users can be notified of new publication events (e.g., publication of a paper that cites one of their papers) using feeds as notification systems. We present three cumulative user studies – requirements elicitation, prototype evaluation, and naturalistic study – in the context of supporting CiteSeer feeds. Our results indicate that users prefer feeds that place target items in query-relevant contexts, and that preferred context varies with type of publication event. We found that users integrated feeds as part of their broader, everyday activities and used them as planning tools to collaborate with others.     

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.