This observational study investigates the methods people use in their workplace to organize web information for re‐use. In addition to the bookmarking and history list tools provided by web browsers, people observed in our study used a variety of other methods and associated tools. For example, several participants emailed web addresses (URLs) along with comments to themselves and to others. Other methods observed included printing out web pages, saving web pages to the hard drive, pasting the address for a web page into a document and pasting the address into a personal web site. Differences emerged between people according to their workplace role and their relationship to the information they were gathering. Managers, for example, depended heavily on email to gather and disseminate information and did relatively little direct exploration of the Web. A functional analysis helps to explain differences in “keeping” behavior between people and to explain the overall diversity of methods observed. People differ in the functions they require according to their workplace role and the tasks they must perform; methods vary widely in the functions they provide. The functional analysis can also help to assess the likely success of various tools, current and proposed.
This paper describes the results of an observational study into the methods people use to manage web information for re-use. People observed in our study used a diversity of methods and associated tools. For example, several participants emailed web addresses (URLs) along with comments to themselves and to others. Other methods observed included printing out web pages, saving web pages to the hard drive, pasting the address for a web page into a document and pasting the address into a personal web site. Ironically, two web browser tools that have been explicitly developed to help users track web information -the bookmarking tool and the history list -were not widely used by participants in this study. A functional analysis helps to explain the observed diversity of methods. Methods vary widely in the functions they provide. For example, a web address pasted into a self-addressed email can provide an important reminding function together with a context of relevance: The email arrives in an inbox which is checked at regular intervals and the email can include a few lines of text that explain the URL's relevance and the actions to be taken. On the other hand, for most users in the study, the bookmarking tool ("Favorites" or "Bookmarks" depending on the browser) provided neither a reminding function nor a context of relevance. The functional analysis can help to assess the likely success of various tools, current and proposed.
We want computer systems that can help us assess the similarity or relevance of existing objects (e.g., documents, functions, commands, etc.) to a statement of our current needs (e.g., the query). Towards this end, a variety of similarity measures have been proposed. However, the relationship between a measure's formula and its performance is not always obvious. A geometric analy sis is advanced and its utility demonstrated through its application to six conventional information retrieval similarity measures and a seventh spreading activation measure. All seven similarity measures work with a representational scheme wherein a query and the database objects are represented as vectors of term weights. A geometric analysis characterizes each similarity measure by the nature of its iso-similarity contours in an n-space containing query and object vectors. This analysis reveals important differences among the similarity measures and suggests conditions in which these differences will affect retrieval performance. The cosine coefficient, for example, is shown to be insensitive to between-document differences in the magnitude of term weights while the inner product measure is sometimes overly affected by such differences.The contextsensitive spreading activation measure may overcome both of these limitations and deserves further study. The geometric analysis is intended to complement, and perhaps to guide, the empirical analysis of similarity measures.
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