The ideal information retrieval user interface will allow people to integrate source materials, discovery techniques, services and people as appropriate for the task they are trying to accomplish. In addition, people often try to accomplish tasks over a span of time and in differing physical locations. The user interface(s) must be flexible, interchangeable, and remain coherent between and across these dimensions.
** Integrate source materials. The ideal interface will:
** Facilitate information use to get work done. The ideal interface will:
Abstract: We propose a user interaction model for browsing based on iterative category-level operations. The motivation comes from two observations: 1) people naturally think in terms of categories, and 2) in browsing, the types of categories that are salient to users change as they browse. We define a set of category-level operations that lets users iteratively view and find results in terms of these changing category types. We also show that we can express some standard IR operations as iteratively applied sequences of a fundamental category-level operation (thus unifying them). Finally, we describe SenseMaker, a prototype interface for browsing heterogeneous sources.
Note: Papers in this series are in development and are not in a final form for publication or general dissemination. They are subject to change. Please do not quote or further distribute them without explicit permission from the authors. This paper was created on: 02/01/96 and last revised on: 2/1/1996
Author's Comments: Submitted to SIGIR '96.
Abstract: In this paper we describe an interface to a heterogeneous digital library. The interface is designed with the following goals in mind: to support user tasks, to smoothly integrate the results of many services, to handle services of widely- varying time scales, to be extensible, and to support sharing and reuse. We discuss each of these goals, and then describe a working prototype interface.
Note: Papers in this series are in development and are not in a final form for publication or general dissemination. They are subject to change. Please do not quote or further distribute them without explicit permission from the authors
This paper was created on: 9/13/95 and last revised on:12/15/1995
Author's Comments: This paper was submitted as a short paper to
the CHI'96 conference. The postscript version that was submitted
as "camera-ready" is available from
http://www-pcd.stanford.edu/cousins/papers/chi95/final.ps
Abstract: Searching over heterogeneous information sources is difficult in part because of the non-uniform query languages. Our approach is to allow users to compose Boolean queries in one rich front-end language. For each user query and target source, we transform the user query into a subsuming query that can be supported by the source but that may return extra documents. The results are then processed by a filter query to yield the correct final result. In this paper we introduce the architecture and associated mechanism for query translation. In particular, we discuss techniques for rewriting predicates in Boolean queries into native subsuming forms, which is a basis of translating complex queries. We have implemented prototype versions of these mechanisms and demonstrated them on heterogeneous Boolean systems.
Note: Papers in this series are in development and are not in a final form for publication or general dissemination. They are subject to change. Please do not quote or further distribute them without explicit permission from the authors.
This paper was created on: 01/25/96 and last revised on:1/28/1996
Author's Comments: Submitted to SIGIR '96.
Abstract: The current exponential growth of the Internet precipitates a need for new tools to help people cope with the volume of information. To complement recent work on creating searchable indexes of the World-Wide Web and systems for filtering incoming e-mail and Usenet news articles, we describe a system which learns to browse the Internet on behalf of a user. Every day it presents a selection of interesting Web pages. The user evaluates each page, and given this feedback the system adapts and attempts to produce better pages the following day. After demonstrating that our system is able to learn a model of a user with a single well-defined interest, we present an initial experiment where over the course of 24 days the output of our system was compared to both randomly-selected and human-selected pages. It consistently performed better than the random pages, and was better than the human-selected pages half of the time.
Note: Papers in this series are in development and are not in a final form for publication or general dissemination. They are subject to change. Please do not quote or further distribute them without explicit permission from the authors.
This paper was created on: 11/9/95 and last revised on:12/4/1995
Author's Comments: This is (hopefully) the final version of this, soon to be a technical note/report.