Search (7 results, page 1 of 1)

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Abstract

In explicit collaborative search, two or more individuals coordinate their efforts toward a shared goal. Every day, Internet users with similar information needs have the potential to collaborate. However, online search is typically performed in solitude. Existing search systems do not promote explicit collaborations, and collaboration opportunities (collabportunities) are missed. In this article, we describe a method to evaluate the feasibility of transforming these collabportunities into recommendations for explicit collaboration. We developed a technique called pseudocollaboration to evaluate the benefits and costs of collabportunities through simulations. We evaluate the performance of our method using three data sets: (a) data from single users' search sessions, (b) data with collaborative search sessions between pairs of searchers, and (c) logs from a large-scale search engine with search sessions of thousands of searchers. Our results establish when and how collabportunities would significantly help or hinder the search process versus searches conducted individually. The method that we describe has implications for the design and implementation of recommendation systems for explicit collaboration. It also connects system-mediated and user-mediated collaborative search, whereby the system evaluates the likely benefits of collaborating for a search task and helps searchers make more informed decisions on initiating and executing such a collaboration.

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Date

20. 1.2015 18:30:22

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Abstract

Support for explicit collaboration in information-seeking activities is increasingly recognized as a desideratum for search systems. Several tools have emerged recently that help groups of people with the same information-seeking goals to work together. Many issues for these collaborative information-seeking (CIS) environments remain understudied. The authors identified awareness as one of these issues in CIS, and they presented a user study that involved 42 pairs of participants, who worked in collaboration over 2 sessions with 3 instances of the authors' CIS system for exploratory search. They showed that while having awareness of personal actions and history is important for exploratory search tasks spanning multiple sessions, support for group awareness is even more significant for effective collaboration. In addition, they showed that support for such group awareness can be provided without compromising usability or introducing additional load on the users.

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Abstract

Communication and coordination are considered essential components of successful collaborations, and provision of awareness is a highly valuable feature of a collaborative information seeking (CIS) system. In this article, we investigate how providing different kinds of awareness support affects people's coordination behavior in a CIS task, as reflected by the communication that took place between them. We describe a laboratory study with 84 participants in 42 pairs with an experimental CIS system. These participants were brought to the laboratory for two separate sessions and given two exploratory search tasks. They were randomly assigned to one of the three systems, defined by the kind of awareness support provided. We analyzed the messages exchanged between the participants of each team by coding them for their coordination attributes. With this coding, we show how the participants employed different kinds of coordination during the study. Using qualitative and quantitative analyses, we demonstrate that the teams with no awareness, or with only personal awareness support, needed to spend more time and effort doing coordination than those with proper group awareness support. We argue that appropriate and adequate awareness support is essential for a CIS system for reducing coordination costs and keeping the collaborators well coordinated for a productive collaboration. The findings have implications for system designers as well as cognitive scientists and CIS researchers in general.

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Abstract

The notions that information seeking is not always a solitary activity and that people working in collaboration for information intensive tasks should be studied and supported have become more prevalent in recent years. Several new research questions, methodologies, and systems have emerged around these notions that may prove to be useful beyond the field of collaborative information seeking (CIS), with relevance to the broader area of information seeking and behavior. This article provides an overview of such key research work from a variety of domains, including library and information science, computer-supported cooperative work, human-computer interaction, and information retrieval. It starts with explanations of collaboration and how CIS fits in different contexts, emphasizing the interactive, intentional, and mutually beneficial nature of CIS activities. Relations to similar and related fields such as collaborative information retrieval, collaborative information behavior, and collaborative filtering are also clarified. Next, the article presents a synthesis of various frameworks and models that exist in the field today, along with a new synthesis of 12 different dimensions of group activities. A discussion on issues and approaches relating to evaluating various parameters in CIS follows. Finally, a list of known issues and challenges is presented to provide an overview of research opportunities in this field.

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Abstract

Most information retrieval (IR) systems consider relevance, usefulness, and quality of information objects (documents, queries) for evaluation, prediction, and recommendation, often ignoring the underlying search process of information seeking. This may leave out opportunities for making recommendations that analyze the search process and/or recommend alternative search process instead of objects. To overcome this limitation, we investigated whether by analyzing a searcher's current processes we could forecast his likelihood of achieving a certain level of success with respect to search performance in the future. We propose a machine-learning-based method to dynamically evaluate and predict search performance several time-steps ahead at each given time point of the search process during an exploratory search task. Our prediction method uses a collection of features extracted from expression of information need and coverage of information. For testing, we used log data collected from 4 user studies that included 216 users (96 individuals and 60 pairs). Our results show 80-90% accuracy in prediction depending on the number of time-steps ahead. In effect, the work reported here provides a framework for evaluating search processes during exploratory search tasks and predicting search performance. Importantly, the proposed approach is based on user processes and is independent of any IR system.

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Abstract

Implicit detection of relevance has been approached by many during the last decade. From the use of individual measures to the use of multiple features from different sources (multimodality), studies have shown the feasibility to automatically detect whether a document is relevant. Despite promising results, it is not clear yet to what extent multimodality constitutes an effective approach compared to unimodality. In this article, we hypothesize that it is possible to build unimodal models capable of outperforming multimodal models in the detection of perceived relevance. To test this hypothesis, we conducted three experiments to compare unimodal and multimodal classification models built using a combination of 24 features. Our classification experiments showed that a univariate unimodal model based on the left-click feature supports our hypothesis. On the other hand, our prediction experiment suggests that multimodality slightly improves early classification compared to the best unimodal models. Based on our results, we argue that the feasibility for practical applications of state-of-the-art multimodal approaches may be strongly constrained by technology, cultural, ethical, and legal aspects, in which case unimodality may offer a better alternative today for supporting relevance detection in interactive information retrieval systems.