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

Exploring knowledge learning in collaborative information seeking process

Knowledge learning is recognized as an important component in people's search process. Existing studies on this topic usually measure the knowledge growth before and after a search. However, there still lacks a fine-grained understanding of users' knowledge change patterns within a search process and users' adoption of different sources for learning. In this on-going project, we are exploring answers to both questions in collaborative information seeking (CIS) since the CIS tasks are usually exploratory, which triggers learning, and involve diverse learning resources such as self-explored search content, partners' search content and explicit communication between them. Through analyzing the data from a controlled laboratory user study with both collaborative and individual information seeking conditions, we demonstrated that users' knowledge keeps growing in both conditions, but they issue significantly more diverse queries in the collaborative condition. Our analysis of users' queries also revealed that the adoption of different learning resources varies at different information seeking stages, and the adoption is influenced by the nature of search tasks too. Finally, we propose several insights for system design to enhance knowledge learning in collaborative information seeking process

Collaborative information seeking with ant colony ranking in real-time

In this paper we propose a new ranking algorithm based on Swarm Intelligence, more specifically on the Ant Colony Optimization technique, to improve search engines’ performances and reduce the information overload by exploiting users’ collective behavior. We designed an online evaluation involving end users to test our algorithm in a real-world scenario dealing with informational queries. The development of a fully working prototype – based on the Wikipedia search engine – demonstrated promising preliminary results

Mobile, ubiquitous information seeking, as a group: the iBingo collaborative video retrieval system

iBingo features two or more users performing collaborative information seeking tasks, using mobile devices, Apple iPod iTouch in our case. The novelty in our work is that the system, called iBingo, mediates the collaborative searches among the users and performs a realtime division of labour among co-searchers so users are presented with documents which are both unique and tailored to the individual. This enables each user to explore unique subsets of the retrieved information space. We demonstrate iBingo mobile collabo-rative search on a video collection from TRECVid 2007

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

Report on the Second International Workshop on the Evaluation on Collaborative Information Seeking and Retrieval (ECol'2017 @ CHIIR)

The 2nd workshop on the evaluation of collaborative information retrieval and seeking (ECol) was held in conjunction with the ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR) in Oslo, Norway. The workshop focused on discussing the challenges and difficulties of researching and studying collaborative information retrieval and seeking (CIS/CIR). After an introductory and scene setting overview of developments in CIR/CIS, participants were challenged with devising a range of possible CIR/CIS tasks that could be used for evaluation purposes. Through the brainstorming and discussions, valuable insights regarding the evaluation of CIR/CIS tasks become apparent ? for particular tasks efficiency and/or effectiveness is most important, however for the majority of tasks the success and quality of outcomes along with knowledge sharing and sense-making were most important ? of which these latter attributes are much more difficult to measure and evaluate. Thus the major challenge for CIR/CIS research is to develop methods, measures and methodologies to evaluate these high order attributes