Beyond traditional collaborative search: Understanding the effect of awareness on multi-level collaborative information retrieval

Although there has been a great deal of research into Collaborative Information Retrieval (CIR) and Collaborative Information Seeking (CIS), the majority has assumed that team members have the same level of unrestricted access to underlying information. However, observations from different domains (e.g. healthcare, business, etc.) have suggested that collaboration sometimes involves people with differing levels of access to underlying information. This type of scenario has been referred to as Multi-Level Collaborative Information Retrieval (MLCIR). To the best of our knowledge, no studies have been conducted to investigate the effect of awareness, an existing CIR/CIS concept, on MLCIR. To address this gap in current knowledge, we conducted two separate user studies using a total of 5 different collaborative search interfaces and 3 information access scenarios. A number of Information Retrieval (IR), CIS and CIR evaluation metrics, as well as questionnaires were used to compare the interfaces. Design interviews were also conducted after evaluations to obtain qualitative feedback from participants. Results suggested that query properties such as time spent on query, query popularity and query effectiveness could allow users to obtain information about team's search performance and implicitly suggest better queries without disclosing sensitive data. Besides, having access to a history of intersecting viewed, relevant and bookmarked documents could provide similar positive effect as query properties. Also, it was found that being able to easily identify different team members and their actions is important for users in MLCIR. Based on our findings, we provide important design recommendations to help develop new CIR and MLCIR interfaces.     

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.     

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.     

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.     

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.     

Role-based results redistribution for collaborative information retrieval

We describe a new approach for algorithmic mediation of a collaborative search process. Unlike most approaches to collaborative IR, we are designing systems that mediate explicitly-defined synchronous collaboration among small groups of searchers with a shared information need. Such functionality is provided by first obtaining different rank-lists based on searchers’ queries, fusing these rank-lists, and then splitting the combined list to distribute documents among collaborators according to their roles. For the work reported here, we consider the case of two people collaborating on a search. We assign them roles of Gatherer and Surveyor: the Gatherer is tasked with exploring highly promising information on a given topic, and the Surveyor is tasked with digging further to explore more diverse information. We demonstrate how our technique provides the Gatherer with high-precision results, and the Surveyor with information that is high in entropy.     

RSS: A framework enabling ranked search on the semantic web

The semantic web not only contains resources but also includes the heterogeneous relationships among them, which is sharply distinguished from the current web. As the growth of the semantic web, specialized search techniques are of significance. In this paper, we present RSS—a framework for enabling ranked semantic search on the semantic web. In this framework, the heterogeneity of relationships is fully exploited to determine the global importance of resources. In addition, the search results can be greatly expanded with entities most semantically related to the query, thus able to provide users with properly ordered semantic search results by combining global ranking values and the relevance between the resources and the query. The proposed semantic search model which supports inference is very different from traditional keyword-based search methods. Moreover, RSS also distinguishes from many current methods of accessing the semantic web data in that it applies novel ranking strategies to prevent returning search results in disorder. The experimental results show that the framework is feasible and can produce better ordering of semantic search results than directly applying the standard PageRank algorithm on the semantic web.     

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.     

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.     

Supporting exploratory video retrieval tasks with grouping and recommendation

In this paper, we present ViGOR (Video Grouping, Organisation and Recommendation), an exploratory video retrieval system. Exploratory video retrieval tasks are hampered by the lack of semantics associated to video and the overwhelming amount of video items stored in these types of collections (e.g. YouTube, MSN video, etc.). In order to help facilitate these exploratory video search tasks we present a system that utilises two complementary approaches: the first a new search paradigm that allows the semantic grouping of videos and the second the exploitation of past usage history in order to provide video recommendations. We present two types of recommendation techniques adapted to the grouping search paradigm: the first is a global recommendation, which couples the multi-faceted nature of explorative video retrieval tasks with the current user need of information in order to provide recommendations, and second is a local recommendation, which exploits the organisational features of ViGOR in order to provide more localised recommendations based on a specific aspect of the user task. Two user evaluations were carried out in order to (1) validate the new search paradigm provided by ViGOR, characterised by the grouping functionalities and (2) evaluate the usefulness of the proposed recommendation approaches when integrated into ViGOR. The results of our evaluations show (1) that the grouping, organisational and recommendation functionalities can result in an improvement in the users’ search performance without adversely impacting their perceptions of the system and (2) that both recommendation approaches are relevant to the users at different stages of their search, showing the importance of using multi-faceted recommendations for video retrieval systems and also illustrating the many uses of collaborative recommendations for exploratory video search tasks.     

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.     

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.     

Personalization of tagging systems

Social media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata.This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative search. We propose a ranking model for each task that integrates the individual user’s tagging history in the recommendation of tags and content, to align its suggestions to the individual user preferences. We demonstrate on two real data sets that for all three tasks, the personalized ranking should take into account both the user’s own preference and the opinion of others.     

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.     

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.     

Building user profiles based on sequences for content and collaborative filtering

Modeling user profiles is a necessary step for most information filtering systems – such as recommender systems – to provide personalized recommendations. However, most of them work with users or items as vectors, by applying different types of mathematical operations between them and neglecting sequential or content-based information. Hence, in this paper we study how to propose an adaptive mechanism to obtain user sequences using different sources of information, allowing the generation of hybrid recommendations as a seamless, transparent technique from the system viewpoint. As a proof of concept, we develop the Longest Common Subsequence (LCS) algorithm as a similarity metric to compare the user sequences, where, in the process of adapting this algorithm to recommendation, we include different parameters to control the efficiency by reducing the information used in the algorithm (preference filter), to decide when a neighbor is considered useful enough to be included in the process (confidence filter), to identify whether two interactions are equivalent (δ-matching threshold), and to normalize the length of the LCS in a bounded interval (normalization functions). These parameters can be extended to work with any type of sequential algorithm.We evaluate our approach with several state-of-the-art recommendation algorithms using different evaluation metrics measuring the accuracy, diversity, and novelty of the recommendations, and analyze the impact of the proposed parameters. We have found that our approach offers a competitive performance, outperforming content, collaborative, and hybrid baselines, and producing positive results when either content- or rating-based information is exploited.     

Meta-Circuit machine: Inferencing human collaborative relationships in heterogeneous information networks

Human collaborative relationship inference is a meaningful task for online social networks and is called link prediction in network science. Real-world networks contain multiple types of interacting components and can be modeled naturally as heterogeneous information networks (HINs). The current link prediction algorithms in HINs fail to effectively extract training samples from snapshots of HINs; moreover, they underutilise the differences between nodes and between meta-paths. Therefore, we propose a meta-circuit machine (MCM) that can learn and fuse node and meta-path features efficiently, and we use these features to inference the collaborative relationships in question-and-answer and bibliographic networks. We first utilise meta-circuit random walks to obtain training samples in which the basic idea is to perform biased meta-path random walks on the input and target network successively and then connect them. Then, a meta-circuit recurrent neural network (mcRNN) is designed for link prediction, which represents each node and meta-path by a dense vector and leverages an RNN to fuse the features of node sequences. Experiments on two real-world networks demonstrate the effectiveness of our framework. This study promotes the investigation of potential evolutionary mechanisms for collaborative relationships and offers practical guidance for designing more effective recommendation systems for online social networks.     

区域科技协同创新体系分析框架研究——以珠三角地区为例

当前,随着新一轮科技革命和产业变革正在加速演进,区域科技协同创新已成为加快区域一体化进程,提升区域国际竞争力的重要途径。区域科技协同创新体系的相关研究也随之剧增,但从政策规划角度分析,面临研究结论纷杂,理论支撑不足的困境。为此,本文基于驱动因素、合作层次以及创新模式三个角度,构建区域科技协同创新体系的三维分析框架,分析区域科技协同创新体系的现状和未来趋势。并以珠三角地区科技协同创新体系为例,阐述三维分析框架对战略规划和政策制度的参考意义。     

Cluster searching strategies for collaborative recommendation systems

In-memory nearest neighbor computation is a typical collaborative filtering approach for high recommendation accuracy. However, this approach is not scalable given the huge number of customers and items in typical commercial applications. Cluster-based collaborative filtering techniques can be a remedy for the efficiency problem, but they usually provide relatively lower accuracy figures, since they may become over-generalized and produce less-personalized recommendations. Our research explores an individualistic strategy which initially clusters the users and then exploits the members within clusters, but not just the cluster representatives, during the recommendation generation stage. We provide an efficient implementation of this strategy by adapting a specifically tailored cluster-skipping inverted index structure. Experimental results reveal that the individualistic strategy with the cluster-skipping index is a good compromise that yields high accuracy and reasonable scalability figures.     

Characterizing the frequency of repeated citations: The effects of journal,subject area,and self-citation

Previous studies have repeatedly demonstrated that the relevance of a citing document is related to the number of times with which the source document is cited. Despite the ease with which electronic documents would permit the incorporation of this information into citation-based document search and retrieval systems, the possibilities of repeated citations remain untapped. Part of this under-utilization may be due to the fact that very little is known regarding the pattern of repeated citations in scholarly literature or how this pattern may vary as a function of journal, academic discipline or self-citation. The current research addresses these unanswered questions in order to facilitate the future incorporation of repeated citation information into document search and retrieval systems. Using data mining of electronic texts, the citation characteristics of nine different journals, covering the three different academic fields (economics, computing, and medicine & biology), were characterized. It was found that the frequency (f) with which a reference is cited N or more times within a document is consistent across the sampled journals and academic fields. Self-citation causes an increase in frequency, and this effect becomes more pronounced for large N. The objectivity, automatability, and insensitivity of repeated citations to journal and discipline, present powerful opportunities for improving citation-based document search.