CoSense: Enhancing Sensemaking for Collaborative Web Search

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The document discusses a study that aimed to understand how sensemaking occurs during collaborative web search tasks. It describes the development of a tool called CoSense to enhance sensemaking. An evaluation found that CoSense helped address challenges identified in an earlier formative study by supporting awareness, temporality, trajectories, contributions and handoffs during synchronous and asynchronous search. The study provided insights into designing features to support collaborative sensemaking during web search.

Technology Design

CoSense: Enhancing Sensemaking for Collaborative Web Search Sharoda A. Paul Meredith Ringel Morris Apr 8, 2009

Getting information off the Internet is like taking a drink from a fire hydrant - Mitchell Kapor

Making sense of information found during a Web search task can be daunting

During collaborative Web search tasks, making sense of information can be even more complex and challenging “ How does sensemaking occur during collaborative Web search task?”

Background: Sensemaking ,[object Object],[object Object],Sensemaking is finding meaning or understanding of information ,[object Object]

Background: Collaborative Sensemaking ,[object Object],[object Object],[object Object],[object Object]

Background: Collaborative Web search ,[object Object],[object Object],[object Object]

What we did ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

SearchTogether (UIST, 2007) Query history Search session Add comments and ratings to Web pages Chat with collaborators

SearchTogether: Features ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Formative study: Design ,[object Object],Phase 1 Phase 2 Group ST ST ST ,[object Object],[object Object],[object Object],Data collection SearchTogether Tools used 6 groups (3 in each group) Participants Vacation planning 2-phase design Task

Formative study: Findings ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

CoSense ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Database SearchTogether CoSense

Evaluation of CoSense ,[object Object],[object Object],[object Object],[object Object],[object Object],Data collection SearchTogether and CoSense Tools used 18 participants (4 in each group) Participants Vacation planning 2-phase design Task

Evaluation of CoSense: Data collection ,[object Object],[object Object],[object Object],[object Object],[object Object],“ Which group member do you think contributed most to the task and why?” “ Which websites generated a lot of discussion?”

Evaluation of CoSense: Findings ,[object Object],[object Object],[object Object],[object Object]

CoSense view usage: During task (1) ,[object Object],[object Object],[object Object],[object Object],[object Object]

CoSense view usage: During task (2) ,[object Object],[object Object],[object Object],[object Object],[object Object]

CoSense view usage: During questionnaire ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

CoSense usage during handoff “ The tag clouds and statistics on how many queries had been done, what were the group’s sites, and what is their popularity, helped me immensely …I was able to plan [the search] better using the websites and keywords ” “ The flow of the search was hard to tell [by looking at SearchTogether]. The timeline in CoSense helped because of the information about order of queries. I identified what had already been done ”

Discussion ,[object Object],[object Object],[object Object],[object Object]

Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Future work ,[object Object],[object Object],[object Object]

Thank you! ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Recent research shows that Collaborative Information Retrieval (CIR), in which two or more users collaborate on the same search task, has become increasingly popular. The presence of both search and collaboration behaviors makes CIR a complex search format, which further drives a critical need to understand CIR's search context. The contextual support for CIR should consider search contexts derived from both team members' search histories (including users' own search histories and partners' search histories) and their explicit collaboration (e.g., chatting). As it stands, existing studies on contextual search support only focus on Individual Information Retrieval (IIR) and only utilize individuals' own search histories. In this paper, we examine the unique search contexts (e.g., partners' search histories and team collaboration histories) in CIR. Based on a user study data collection with 54 participants, we find that compared to the use of individuals' own search histories, CIR contextual support is more effective when utilizing partners' search histories and teams' collaboration behaviors. More interestingly, though the explicit communication information (i.e., chat content) often involves massive noisy information, involving such noise does not affect the ranking of relevant documents since it also does not appear in relevant documents.

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Collaborative information retrieval systems often rely on division of labor policies. Such policies allow work to be divided among collaborators with the aim of preventing redundancy and optimizing the synergic effects of collaboration. Most of the underlying methods achieve these goals by the means of explicit vs. implicit role-based mediation. In this paper, we investigate whether and how different factors, such as users' behavior, search strategies, and effectiveness, are related to role assignment within a collaborative exploratory search. Our main findings suggest that: (1) spontaneous and cohesive implicit roles might emerge during the collaborative search session implying users with no prior roles, and that these implicit roles favor the search precision, (2) role drift might occur alongside the search session performed by users with prior-assigned roles.

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The research topic of this document deals with a particular setting of information retrieval (IR), referred to as collaborative information retrieval (CIR), in which a set of multiple collaborators share the same information need. Collaboration is particularly used in case of complex tasks in which an individual user may have insufficient knowledge and may benefit from the expertise/knowledge or complementarity of other collaborators. This multi-user context rises several challenges in terms of search interfaces as well as ranking models, since new paradigms must be considered, namely division of labor, sharing of knowledge and awareness. These paradigms aim at avoiding redundancy between collaborators in order to reach a synergic effect within the collaboration process. Several approaches have been proposed in the literature. First, search interfaces have been oriented towards a user mediation in order to support collaborators’ actions through information storage or communication tools. Second, more close to our contributions, previous work focus on the information access issue by designing ranking models adapted to collaborative environments dealing with the challenges of (1) personalizing result set to collaborators, (2) favoring the sharing of knowledge, (3) dividing the labor among collaborators and/or (4) considering particular roles of collaborators within the information seeking process. In this thesis, we focus, more particularly, on two main aspects of the collaboration : — The expertise of collaborators by proposing retrieval models adapted to the domain expertise level of collaborators. The expertise levels might be vertical, in the case of domain expert and novice, or horizontal when collaborators have different subdomain expertise. We, therefore, propose two CIR models on two steps including a document relevance scoring with respect to each role and a document allocation to user roles through the Expectation–Maximization (EM) learning method applied on the document relevance scoring in order to assign documents to the most likely suited user. — The complementarity of collaborators throughout the information seeking process by mining their roles on the assumptions that collaborators might be different and complementary in some skills. We propose two algorithms based either on predefined roles or latent roles which (1) learns about the roles of the collaborators using various search-related features for each individual involved in the search session, and (2) adapts the document ranking to the mined roles of collaborators.

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A great amount of research in the IR domain mostly dealt with both the design of enhanced document ranking models allowing search improvement through user-to-system collaboration. However, in addition to user-to-system form of collaboration, user-to-user collaboration is increasingly acknowledged as an effective mean for gathering the complementary skills and/or knowledge of individual users in order to solve complex search tasks.This tutorial will first give an overview of the ways into collaboration has been implemented in IR models with the attempt of improving the search outcomes with respect to several tasks and related frameworks (ad-hoc search, group-based recommendation, social search, collaborative search). Second, as envisioned in collaborative IR domain (CIR), we will focus on the theoretical models that support and drive user-to-user collaboration in order to perform shared IR tasks. Third, we will develop a road map on emerging and relevant topics addressing issues related to collaboration design. Our goal is to provide participants with concepts and motivation allowing them to investigate this emerging IR domain as well as giving them some clues on how to tackle issues related to the optimization of collaborative tasks. More specifically, the tutorial aims to: 1. Give an overview of the key concept of collaboration in IR and related research topics; 2. Present state-of-the art CIR techniques and models; 3. Discuss about the emerging topics that deal with collaboration; 4. Point out some challenges ahead.

In the library sciences, information seeking has long been recognized as a collaborative activity, and recent work has attempted to model group information seeking behavior. Until recently, technological support for group-based information seeking has been limited to collaborative filtering and "social search" applications. In the past two years, however, a new kind of technologically-mediated collaborative search has been demonstrated in systems such as SearchTogether and Cerchiamo. This approach is more closely grounded in the library science interpretation of collaboration: rather than inferring commonality of interest through similarity of queries (social search), the new approach assumes an explicitly-shared information need for a group. This allows the system to focus on mediating the collaboration rather than detecting its presence. In this talk, we describe a model that captures both user behavior and system architecture, describe its relationship to other models of information seeking, and use it to classify existing multi-user search systems. We also describe implications this model has for design and evaluation of new collaborative information seeking systems. Talk presented at NIST on October 22, 2009.

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CoSense: Enhancing Sensemaking for Collaborative Web Search

1. CoSense: Enhancing Sensemaking for Collaborative Web Search Sharoda A. Paul Meredith Ringel Morris Apr 8, 2009

2. Getting information off the Internet is like taking a drink from a fire hydrant - Mitchell Kapor

3. Making sense of information found during a Web search task can be daunting

4. During collaborative Web search tasks, making sense of information can be even more complex and challenging “ How does sensemaking occur during collaborative Web search task?”

10. SearchTogether (UIST, 2007) Query history Search session Add comments and ratings to Web pages Chat with collaborators

11.

12.

13.

14.

15. Search strategies view (1)

16. Search strategies view (2)

17. Timeline view

18. Workspace view

19. Chat-centric view

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21.

22.

23.

24.

25.

26. CoSense usage during handoff “ The tag clouds and statistics on how many queries had been done, what were the group’s sites, and what is their popularity, helped me immensely …I was able to plan [the search] better using the websites and keywords ” “ The flow of the search was hard to tell [by looking at SearchTogether]. The timeline in CoSense helped because of the information about order of queries. I identified what had already been done ”

27.

28.

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30.

31.

Editor's Notes

Good afternoon ladies and gentleman, thank you for attending this talk. I am Sharoda Paul from Penn State University and I’m going to present our paper ‘Cosense: Enhancing Sensemaking in Collaborative Web Search, written with my co-author Meredith Morris of Microsoft Research. The study I am presenting explored how people make sense of information found during collaborative Web search tasks. For instance, if a group of friends are planning to go on a vacation, they might each go online and search the Web for fun activities to add to their vacation itinerary. The question we explored in our study is ‘how do people make sense of the information generated during such a collaborative Web search task.

CoSense: Enhancing Sensemaking for Collaborative Web Search

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