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

Just an Update on PMING Distance for Web-based Semantic Similarity in Artificial Intelligence and Data Mining

One of the main problems that emerges in the classic approach to semantics is the difficulty in acquisition and maintenance of ontologies and semantic annotations. On the other hand, the Internet explosion and the massive diffusion of mobile smart devices lead to the creation of a worldwide system, which information is daily checked and fueled by the contribution of millions of users who interacts in a collaborative way. Search engines, continually exploring the Web, are a natural source of information on which to base a modern approach to semantic annotation. A promising idea is that it is possible to generalize the semantic similarity, under the assumption that semantically similar terms behave similarly, and define collaborative proximity measures based on the indexing information returned by search engines. The PMING Distance is a proximity measure used in data mining and information retrieval, which collaborative information express the degree of relationship between two terms, using only the number of documents returned as result for a query on a search engine. In this work, the PMINIG Distance is updated, providing a novel formal algebraic definition, which corrects previous works. The novel point of view underlines the features of the PMING to be a locally normalized linear combination of the Pointwise Mutual Information and Normalized Google Distance. The analyzed measure dynamically reflects the collaborative change made on the web resources

Towards a Model of Understanding Social Search

Search engine researchers typically depict search as the solitary activity of an individual searcher. In contrast, results from our critical-incident survey of 150 users on Amazon's Mechanical Turk service suggest that social interactions play an important role throughout the search process. Our main contribution is that we have integrated models from previous work in sensemaking and information seeking behavior to present a canonical social model of user activities before, during, and after search, suggesting where in the search process even implicitly shared information may be valuable to individual searchers.Comment: Presented at 1st Intl Workshop on Collaborative Information Seeking, 2008 (arXiv:0908.0583

Combining relevance information in a synchronous collaborative information retrieval environment

Traditionally information retrieval (IR) research has focussed on a single user interaction modality, where a user searches to satisfy an information need. Recent advances in both web technologies, such as the sociable web of Web 2.0, and computer hardware, such as tabletop interface devices, have enabled multiple users to collaborate on many computer-related tasks. Due to these advances there is an increasing need to support two or more users searching together at the same time, in order to satisfy a shared information need, which we refer to as Synchronous Collaborative Information Retrieval. Synchronous Collaborative Information Retrieval (SCIR) represents a significant paradigmatic shift from traditional IR systems. In order to support an effective SCIR search, new techniques are required to coordinate users' activities. In this chapter we explore the effectiveness of a sharing of knowledge policy on a collaborating group. Sharing of knowledge refers to the process of passing relevance information across users, if one user finds items of relevance to the search task then the group should benefit in the form of improved ranked lists returned to each searcher. In order to evaluate the proposed techniques we simulate two users searching together through an incremental feedback system. The simulation assumes that users decide on an initial query with which to begin the collaborative search and proceed through the search by providing relevance judgments to the system and receiving a new ranked list. In order to populate these simulations we extract data from the interaction logs of various experimental IR systems from previous Text REtrieval Conference (TREC) workshops

Synchronous collaborative information retrieval with relevance feedback

Collaboration has been identified as an important aspect in information seeking. People meet to discuss and share ideas and through this interaction an information need is quite often identified. However the process of resolving this information need, through interacting with a search engine and performing a search task, is still an individual activity. We propose an environment which allows users to collaborate to satisfy a shared information need. We discuss ways to divide the search task amongst collaborators and propose the use of relevance feedback, a common information retrieval process, to enable the transfer of knowledge across collaborators during a search session. We describe the process by which co-searchers can collaborate effectively with little redundancy and how we can combine relevance judgements from multiple searchers into a coherent model for synchronous collaborative information retrieva

Collaborative Indexing as a Framework for Search and Knowledge Management

Information search relevance is a key challenge for information systems researchers. We propose a framework for development of a new architecture for search, collaborative indexing, employing the collaborative thinking of informed individuals to develop indices. Creator circles of individuals will mark pages of relevance to a topic, or pages that usefully answer questions, through interaction with their web browser. These marked pages will create and continuously add to a domain specific search index. Through searches of this index by members of the creator circles, other members of the organization, or the public, we propose the results returned will have greater relevance and usefulness in finding information, collaborative learning, and collaborative problem solving

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

Towards quantifying the impact of non-uniform information access in collaborative information retrieval

The majority of research into Collaborative Information Retrieval (CIR) has assumed a uniformity of information access and visibility between collaborators. However in a number of real world scenarios, information access is not uniform between all collaborators in a team e.g. security, health etc. This can be referred to as Multi-Level Collaborative Information Retrieval (MLCIR). To the best of our knowledge, there has not yet been any systematic investigation of the effect of MLCIR on search outcomes. To address this shortcoming, in this paper, we present the results of a simulated evaluation conducted over 4 different non-uniform information access scenarios and 3 different collaborative search strategies. Results indicate that there is some tolerance to removing access to the collection and that there may not always be a negative impact on performance. We also highlight how different access scenarios and search strategies impact on search outcomes

An Empirical Investigation of Collaborative Web Search Tool on Novice\u27s Query Behavior

In the past decade, research efforts dedicated to studying the process of collaborative web search have been on the rise. Yet, a limited number of studies have examined the impact of collaborative information search processes on novices’ query behaviors. Studying and analyzing factors that influence web search behaviors, specifically users’ patterns of queries when using collaborative search systems can help with making query suggestions for group users. Improvements in user query behaviors and system query suggestions help in reducing search time and increasing query success rates for novices. This thesis investigates the influence of collaboration between experts and novices as well as the use of a collaborative web search tool on novices’ query behavior. We used SearchTeam as our collaborative search tool. This empirical study involves four collaborative team conditions: SearchTeam and expert-novice team, SearchTeam and novice-novice team, traditional and expert-novice team, and traditional and novice-novice team. We analyzed participants’ query behavior in two dimensions: quantitatively (e.g. the query success rate), and qualitatively (e.g. the query reformulation patterns). The findings of this study reveal that the successful query rate is higher in expert-novice collaborative teams, who used the collaborative search tools. Participants in expert-novice collaborative teams who used the collaborative search tools, required less time to finalize all tasks compared to expert-novice collaborative teams, who used the traditional search tools. Self-issued queries and chat logs were major sources of terms that novice participants in expert-novice collaborative teams who used the collaborative search tools used. Novices as part of expert-novice pairs who used the collaborative search tools, employed New and Specialization more often as query reformulation patterns. The results of this study contribute to the literature by providing detailed investigation regarding the influence of utilizing collaborative search tool (SearchTeam) in the context of software troubleshooting and development. This study highlights the possible collaborative information seeking (CIS) activities that may occur among software developers’ interns and their mentors. Furthermore, our study reveals that there are specific features, such as awareness and built-in instant messaging (IM), offered by SearchTeam that can promote the CIS activities among participants and help increase novices’ query success rates. Finally, we believe the use of CIS tools, designed to support collaborative search actions in big software development companies, has the potential to improve the overall novices’ query behavior and search strategies