Collaborative Categorization on the Web

Collaborative categorization is an emerging direction for research and innovative applications. Arguably, collaborative categorization on the Web is an especially promising emerging form of collaborative Web systems because of both, the widespread use of the conventional Web and the emergence of the Semantic Web providing with more semantic information on Web data. This paper discusses this issue and proposes two approaches: collaborative categorization via category merging and collaborative categorization proper. The main advantage of the first approach is that it can be rather easily realized and implemented using existing systems such as Web browsers and mail clients. A prototype system for collaborative Web usage that uses category merging for collaborative categorization is described and the results of field experiments using it are reported. The second approach, called collaborative categorization proper, however, is more general and scales better. The data structure and user interface aspects of an approach to collaborative categorization proper are discussed

"Stuff goes into the computer and doesn't come out": a cross-tool study of personal information management

This paper reports a study of Personal Information Management (PIM), which advances research in two ways: (1) rather than focusing on one tool, we collected cross-tool data relating to file, email and web bookmark usage for each participant, and (2) we collected longitudinal data for a subset of the participants. We found that individuals employ a rich variety of strategies both within and across PIM tools, and we present new strategy classifications that reflect this behaviour. We discuss synergies and differences between tools that may be useful in guiding the design of tool integration. Our longitudinal data provides insight into how PIM behaviour evolves over time, and suggests how the supporting nature of PIM discourages reflection by users on their strategies. We discuss how the promotion of some reflection by tools and organizations may benefit users

Social Bookmarking Induced Active Page Ranking

Social bookmarking services have recently made it possible for us to register and share our own bookmarks on the web and are attracting attention. The services let us get structured data: (URL, Username, Timestamp, Tag Set). And these data represent user interest in web pages. The number of bookmarks is a barometer of web page value. Some web pages have many bookmarks, but most of those bookmarks may have been posted far in the past. Therefore, even if a web page has many bookmarks, their value is not guaranteed. If most of the bookmarks are very old, the page may be obsolete. In this paper, by focusing on the timestamp sequence of social bookmarkings on web pages, we model their activation levels representing current values. Further, we improve our previously proposed ranking method for web search by introducing the activation level concept. Finally, through experiments, we show effectiveness of the proposed ranking method

Content repositories and social networking : can there be synergies?

This paper details the novel application of Web 2.0 concepts to current services offered to Social Scientists by the ReDReSS project, carried out by the Centre for e-Science at Lancaster University. We detail plans to introduce Social Bookmarking and Social Networking concepts into the repository software developed by the project. This will result in the improved discovery of e-Science concepts and training to Social Scientists and allow for much improved linking of resources in the repository. We describe plans that use Social Networking and Social Bookmarking concepts, using Open Standards, which will promote collaboration between researchers by using information gathered on user’s use of the repository and information about the user. This will spark collaborations that would not normally be possible in the academic repository context

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Visualizing recommendations to support exploration, transparency and controllability

Research on recommender systems has traditionally focused on the development of algorithms to improve accuracy of recommendations. So far, little research has been done to enable user interaction with such systems as a basis to support exploration and control by end users. In this paper, we present our research on the use of information visualization techniques to interact with recommender systems. We investigated how information visualization can improve user understanding of the typically black-box rationale behind recommendations in order to increase their perceived relevance and meaning and to support exploration and user involvement in the recommendation process. Our study has been performed using TalkExplorer, an interactive visualization tool developed for attendees of academic conferences. The results of user studies performed at two conferences allowed us to obtain interesting insights to enhance user interfaces that integrate recommendation technology. More specifically, effectiveness and probability of item selection both increase when users are able to explore and interrelate multiple entities - i.e. items bookmarked by users, recommendations and tags. Copyright © 2013 ACM

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