Collaborative Information Retrieval: Concepts, Models and Evaluation

International audienceRecent work have shown the potential of collaboration for solving complex or exploratory search tasks allowing to achieve synergic effects with respect to individual search, which is the prevalent information retrieval (IR) setting this last decade. This interactive multiuser context gives rise to several challenges in IR. One main challenge relies on the adaptation of IR techniques or models [8] in order to build algo-rithmic supports of collaboration distributing documents among users. The second challenge is related to the design of Collaborative Information Retrieval (CIR) models and their effectiveness evaluation since individual IR frameworks and measures do not totally fit with the collaboration paradigms. In this tutorial, we address the second challenge and present first a general overview of collaborative search introducing the main underlying notions. Then, we focus on related work dealing with collaborative ranking models and their effectiveness evaluation. Our primary objective is to introduce these notions by highlighting how and why they should be different from individual IR in order to give participants the main clues for investigating new research directions in this domain with a deep understanding of current CIR work

Understanding the Impact of the Role Factor in Collaborative Information Retrieval

International audienceCollaborative 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

Synchronous collaborative information retrieval: techniques and evaluation

Synchronous Collaborative Information Retrieval refers to systems that support multiple users searching together at the same time in order to satisfy a shared information need. To date most SCIR systems have focussed on providing various awareness tools in order to enable collaborating users to coordinate the search task. However, requiring users to both search and coordinate the group activity may prove too demanding. On the other hand without effective coordination policies the group search may not be effective. In this paper we propose and evaluate novel system-mediated techniques for coordinating a group search. These techniques allow for an effective division of labour across the group whereby each group member can explore a subset of the search space.We also propose and evaluate techniques to support automated sharing of knowledge across searchers in SCIR, through novel collaborative and complementary relevance feedback techniques. In order to evaluate these techniques, we propose a framework for SCIR evaluation based on simulations. To populate these simulations we extract data from TREC interactive search logs. This work represent the first simulations of SCIR to date and the first such use of this TREC data

Collaborative Information Retrieval: Frameworks, Theoretical Models, and Emerging Topics

International audienceA 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

Query Generation as Result Aggregation for Knowledge Representation

Knowledge representations have greatly enhanced the fundamental human problem of information search, profoundly changing representations of queries and database information for various retrieval tasks. Despite new technologies, little thought has been given in the field of query recommendation – recommending keyword queries to end users – to a holistic approach that recommends constructed queries from relevant snippets of information; pre-existing queries are used instead. Can we instead determine relevant information a user should see and aggregate it into a query? We construct a general framework leveraging various retrieval architectures to aggregate relevant information into a natural language query for recommendation. We test this framework in text retrieval, aggregating text snippets and comparing output queries to user generated queries. We show that an algorithm can generate queries more closely resembling the original and give effective retrieval results. Our simple approach shows promise for also leveraging knowledge structures to generate effective query recommendations

Large High Resolution Displays for Co-Located Collaborative Intelligence Analysis

Large, high-resolution vertical displays carry the potential to increase the accuracy of collaborative sensemaking, given correctly designed visual analytics tools. From an exploratory user study using a fictional intelligence analysis task, we investigated how users interact with the display to construct spatial schemas and externalize information, as well as how they establish shared and private territories. We investigated the spatial strategies of users partitioned by tool type used (document- or entity-centric). We classified the types of territorial behavior exhibited in terms of how the users interacted with the display (integrated or independent workspaces). Next, we examined how territorial behavior impacted the common ground between the pairs of users. Finally, we recommend design guidelines for building co-located collaborative visual analytics tools specifically for use on large, high-resolution vertical displays

Recommendation, collaboration and social search

This chapter considers the social component of interactive information retrieval: what is the role of other people in searching and browsing? For simplicity we begin by considering situations without computers. After all, you can interactively retrieve information without a computer; you just have to interact with someone or something else. Such an analysis can then help us think about the new forms of collaborative interactions that extend our conceptions of information search, made possible by the growth of networked ubiquitous computing technology. Information searching and browsing have often been conceptualized as a solitary activity, however they always have a social component. We may talk about 'the' searcher or 'the' user of a database or information resource. Our focus may be on individual uses and our research may look at individual users. Our experiments may be designed to observe the behaviors of individual subjects. Our models and theories derived from our empirical analyses may focus substantially or exclusively on an individual's evolving goals, thoughts, beliefs, emotions and actions. Nevertheless there are always social aspects of information seeking and use present, both implicitly and explicitly. We start by summarizing some of the history of information access with an emphasis on social and collaborative interactions. Then we look at the nature of recommendations, social search and interfaces to support collaboration between information seekers. Following this we consider how the design of interactive information systems is influenced by their social elements

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 a recent 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 user profiles to analyse support for different group dynamics. After presenting an analysis of SearchTogether, we reflect on its accuracy, showing that the framework identified 8 known truths, 8 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