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Knowledge workers often need to find, organize, and work with heterogeneous resources from diverse services, information stores, and repositories. This paper ana- lyzes two problems that knowledge workers frequently encounter: difficulty in finding all relevant resources across diverse services, and difficulty in formulating and executing searches...
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Context 1
... 7.5 chat window (see Figure 3) that allows users to trigger a "Surf in Malibu" action on their buddies. This also submits a search request to the publish / subscribe bus and Malibu extensions can perform a similar search on the buddy's identity, showing items related to the searched person. ...
Citations
... Previous research on activity-centric collaboration (e.g. [1, 12, 18, 25, 31]) supports knowledge workers with context switching and resource rediscovery by organizing and integrating resources, tools, and people around the computational concept of a work activity. Many of these approaches have in common that they provide some structure within which all resources of an activity may be collectively located and (re)discovered. ...
... As a consequence, much information that is part of the cognitive model or otherwise related to the activity, or required to complete the activity might not get captured or displayed in the formal representation. We call this problem the " Representation Gap " [31]. In order to satisfy their information needs beyond what is represented in an activity, knowledge workers typically conduct a search using standard web or desktop tools. ...
... Tags are socially filtered content descriptors. We were very interested in the predictive power of tags [21, 27, 30, 31, 35] compared to the other data fields. Our activity predictor and resource recommender prototype is integrated into a contextual user interface in a desktop side bar [31] for easy access, displaying predicted activities and search results. ...
Knowledge workers perform many different activities daily. Each activity defines a distinct work context with different information needs. In this paper we leverage users' activity representations, stored in an activity management system, to automatically recommend resources to support knowl- edge workers in their current activity. We developed a col- laborative activity predictor to both predict the current work activity and measure a resource's relevance to a spe- cific activity. Relevant resources are then displayed in a contextual side bar on the desktop. We describe the system, our new activity-centric search algorithm, and experimental results based on the data from 50 real users.
... In this paper, we present a system and algorithm for prioritizing shared activities using machine learning techniques. We implemented our approach on top of the Lotus Connections Activities system (Lotus Activities) [26] and we integrated our user interface into Malibu ([16], [30]), a desktop client that provides access to Lotus Activities. We formalize the activity prioritization problem as a learning-to-rank problem, and we use a ...
... Our activity prioritization system is implemented as a Malibu plug-in that enhances the activity view of the original Malibu version of [16] and [30].Figure 3 shows the modified activity view. It supports both manually-specified priorities and machine predicted priorities using the algorithm described in this paper. ...
Activity-centric collaboration environments help knowledge workers to manage the context of their shared work activities by providing a representation for an activity and its resources. Activity management systems provide more structure and organization than email to execute the shared activity but, as the number of shared activities increases, it becomes more and more difficult for users to focus on important activities that need their attention. This paper describes a personalized activity prioritization approach implemented on top of the Lotus Connections Activities management system. Our prototype implementation allows each user to view activities ordered by her/his predicted priorities. The predictions are made using a ranking Support Vector Machine model trained with the user's past interactions with the activities system. We describe the prioritization interface and the results of an offline experiment based on data from 13 users over 6-months. Our results show that our feature set derived from shared activity structures can significantly increase prediction accuracy compared to a recency baseline.
... Examples of missed concept-level overlaps included different references for product names and projects (e.g., for an enterprise instant-messaging product, " Sametime75 " vs. " st75 " vs. " st7.5 " vs. " buddylist " vs. " buddy " vs. " cbp2006 instant-messaging [customer-reference] " ). Other research within two of these services has shown that people devote considerable effort into writing tags [21], that people use the tags to search for relevant items across services [26], and that in some cases people use the tags as part of managing their reputations within the enterprise [10, 25]. We are left with the apparent paradox that people work hard to write tags, but seem not to do this consistently for related aspects of their work that are stored in different services. ...
... Our results suggest that simple tag re-use may prove more difficult than anticipated. Indeed, our work on an experimental aggregation service based on tags showed that, for some users, sparse tag-based search results were a problem [26]. We consider three distinct strategies for supporting tag re-use: support during the task of entering a tag into a bookmark; support during the storage of a tag into a tagging-service database; and support during the task of conducting a tag-based search. ...
We compare four tagging-based enterprise services, which respectively stored bookmarks to webpages and documents, to people, to blog entries, and to hierarchically-structured activity records. Analysis of user data and tag data showed relatively small overlaps in tags used. Conventional normalization strategies produced only modest improvement. These results suggest difficulties in combining exploratory searches across multiple social-tagging services. We recommend strategies for cross-service tag integration at the points of tag storage and tag search, rather than at the conventional point of tag entry. We close with a research agenda around this strategy.
Most knowledge repositories focus on the role of knowledge-creators. In this paper, by contrast, we examined the work of Lurkers in an enterprise file-sharing service, and we compared their lurking behaviors to the lurking behaviors of users who uploaded files (Uploaders), and users who contributed metadata about files (Contributors). For comparability, we restricted our analyses to the consuming behaviors that are common to the three roles (Uploaders, Contributors, and Lurkers). Independent principal components analysis showed highly similar seven-factor solutions of lurking activities across all three roles, although the relative emphases of those factors varied across roles. Uploaders tended to view and download more groups of files, showed less emphasis on searching for files, and tended to work directly with the file-sharing application, unmediated by external applications. Contributors showed the opposite pattern: more emphasis on searching and responding to recommendations from other users, often via a form of remote access. Lurkers' lurking behaviors were less intense, and showed little difference in emphases among the lurker factors. We use these results, and the published research literature, to motivate a research agenda for lurkers in social media.
We report on a social-software file-sharing service within a large company. User-created collections of files were associated
with increased usage of the uploaded files, especially the sharing of files from one employee to another. Employees innovated
in the use of the collections features as “information curators,” an emergent lead-user role in which one employee creates
named, described collections of resource for use by other employees. This role suggests new work practices and new features.
