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this article we focus on the middle part of the answer type spectrum by posing the question of how to design a search system and interface that provide a "browsing the shelves" sensation for large collections of information items. We first summarize what is known from usability results about how to design good search user interfaces. We then illustrate these principles with a browse-and-search interface framework we have developed that has been successful in preliminary usability studies

We developed and evaluated seven interfaces for integrating semantic category information with Web search results. List interfaces were based on the familiar ranked-listing of search results, sometimes augmented with a category name for each result. Category interfaces also showed page titles and/or category names, but re-organized the search results so that items in the same category were grouped together visually. Our user studies show that all Category interfaces were more effective than List interfaces even when lists were augmented

We demonstrate a new browsing technique for devices with small displays such as PDAs or cellular phones. We concentrate on end-game browsing, where the user is close to or on the target page. We make browsing more efficient and easier by Accordion Summarization. In this technique the Web page is first represented as a short summary. The user can then drill down to discover relevant parts of the page. If desired, keywords can be highlighted and exposed automatically. We discuss our techniques, architecture, interface facilities, and the result of user evaluations. We measured a 57 % improvement in browsing speed and 75% reduction in input effort.

We present a design and implementation for displaying and manipulating HTML pages on small handheld devices such as personal digital assistants (PDAs), or cellular phones. We introduce methods for summarizing parts of Web pages and HTML forms. Each Web page is broken into text units that can each be hidden, partially displayed, made fully visible, or summarized. A variety of methods are introduced that summarize the text units. In addition, HTML forms are also summarized by displaying just the text labels that prompt the use for input. We tested the relative performance of the summarization methods by asking human subjects to accomplish single-page information search tasks. We found that the combination of keywords and single-sentence summaries provides significant improvements in access times and number of required pen actions, as compared to other schemes. Our experiments also show that our algorithms can identify the appropriate labels for forms in 95 % of the cases, allowing effective form support for small screens.

We have developed an in6 vative searchin terface that allowsnAOz5z ert users to move through large in97z86 tion spacesin a flexible manle without feelin lost. The design goal was to o#er users a "browsin the shelves" experien5 seamlessly in tegrated with focused search. Key to achievin our goal is the explicit exposure of hierarchical faceted metadatain a manz6 that is in tuitive an in vitin to users. After several iteration of design an testinA the usability results are strikinA] positive. We believe our approach marks a major step forward in search userin terfacesan can serve as a model for web-based collection of up to 100,000 items.

We describe Castanet, an algorithm for automatically generating hierarchical faceted metadata from textual descriptions of items, to be incorporated into browsing and navigation interfaces for large information collections. From an existing lexical database (such as WordNet), Castanet carves out a structure that reflects the contents of the target information collection; moderate manual modifications improve the outcome. The algorithm is simple yet effective: a study conducted with 34 information architects finds that Castanet achieves higher quality results than other automated category creation algorithms, and 85 % of the study participants said they would like to use the system for their work. 1

Abstract. Using keyword search to find relevant objects in digital libraries often results in way too large result sets. Based on the metadata associated with such objects, the faceted search paradigm allows users to structure and filter the result set, for example, using a publication type facet to show only books or videos. These facets usually focus on clear-cut characteristics of digital items, however it is very difficult to also organize the actual semantic content information into such a facet. The Semantic GrowBag approach, presented in this paper, uses the keywords provided by many authors of digital objects to automatically create light-weight topic categorization systems as a basis for a meaningful and dynamically adaptable topic facet. Using such emergent semantics enables an alternative way to filter large result sets according to the objects ’ content without the need to manually classify all objects with respect to a pre-specified vocabulary. We present the details of our algorithm using the DBLP collection of computer science documents and show some experimental evidence about the quality of the achieved results.

When search results against digital libraries and web resources have limited metadata, augmenting them with meaningful and stable category information can enable better overviews and support user exploration. This paper proposes six “fast-feature” techniques that use only features available in the search result list, such as title, snippet, and URL, to categorize results into meaningful categories. They use credible knowledge resources, including a US government organizational hierarchy, a thematic hierarchy from the Open Directory Project (ODP) web directory, and personal browse histories, to add valuable metadata to search results. In three tests the percent of results categorized for five representative queries was high enough to suggest practical benefits: general web search (76-90%), government web search (39-100%), and the Bureau of Labor Statistics website (48-94%). An additional test submitted 250 TREC queries to a search engine and successfully categorized 66 % of the top 100 using the ODP and 61 % of the top 350. Fast-feature techniques have been implemented in a prototype search engine. We propose research directions to improve categorization rates and make suggestions about how web site designers could re-organize their sites to support fast categorization of search results.

Current search tools on the Web, such as general-purpose search engines (e.g. Google) and domain-specific portals (e.g. MEDLINEplus), do not provide search procedures that guide users to form appropriately ordered sub-goals. The lack of such procedural knowledge often leads users searching in unfamiliar domains to retrieve incomplete information. In critical domains such as in healthcare, such ineffective searches can have dangerous consequences. To address this situation, we developed a new type of domain portal called a Strategy Hub. Strategy Hubs provide the critical search procedures and associated high-quality links that enable users to find comprehensive and accurate information. This paper describes how we collaborated with skin cancer physicians to systematically identify generalizeable search procedures to find comprehensive information about melanoma, and how these search procedures were made available through the Strategy Hub for healthcare. A pilot study suggests that this approach can improve the efficacy, efficiency, and satisfaction of even expert searchers. We conclude with insights on how to refine the design of the Strategy Hub, and how it can be used to provide search procedures across domains.