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 report on a study of how people look for information within email, files, and the Web. When locating a document or searching for a specific answer, people relied on their contextual knowledge of their information target to help them find it, often associating the target with a specific document. They appeared to prefer to use this contextual information as a guide in navigating locally in small steps to the desired document rather than directly jumping to their target. We found this behavior was especially true for people with unstructured information organization. We discuss the implications of our findings for the design of personal information management tools.

Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology --- PIPE (`Personalization is Partial Evaluation') --- for personalization. Personalization systems are designed and implemented in PIPE by modeling an information-seeking interaction in a programmatic representation. The representation supports the description of information-seeking activities as partial information and their subsequent realization by partial evaluation, a technique for specializing programs. We describe the modeling methodology at a conceptual level and outline representational choices. We present two application case studies that use PIPE for personalizing web sites and describe how PIPE suggests a novel evaluation criterion for information system designs. Finally, we mention several fundamental implications of adopting the PIPE model for personalization and when it is (and is not) applicable.

Personalization constitutes the mechanisms and technologies necessary to customize information access to the end-user. It can be defined as the automatic adjustment of information content, structure, and presentation tailored to the individual. In this chapter, we study personalization from the viewpoint of personalizing interaction. The survey covers mechanisms for information-finding on the web, advanced information retrieval systems, dialogbased applications, and mobile access paradigms. Specific emphasis is placed on studying how users interact with an information system and how the system can encourage and foster interaction. This helps bring out the role of the personalization system as a facilitator which reconciles the user's mental model with the underlying information system's organization. Three tiers of personalization systems are presented, paying careful attention to interaction considerations. These tiers show how progressive levels of sophistication in interaction can be achieved. The chapter also surveys systems support technologies and niche application domains.

Personalization constitutes the mechanisms necessary to automatically customize information content, structure, and presentation to the end-user to reduce information overload. Unlike traditional approaches to personalization, the central theme of our approach is to model a website as a program and conduct website transformation for personalization by program transformation (e.g., partial evaluation, program slicing). The goal of this paper is study personalization through a program transformation lens, and develop a formal model, based on program transformations, for personalized interaction with hierarchical hypermedia. The specific research issues addressed involve identifying and developing program representations and transformations suitable for classes of hierarchical hypermedia, and providing supplemental interactions for improving the personalized experience. The primary form of personalization discussed is out-of-turn interaction – a technique which empowers a user navigating a hierarchical website to postpone clicking on any of the hyperlinks presented on the current page and, instead, communicate the

Personalization customizes information access. The PIPE (`Personalization is Partial Evaluation') modeling methodology represents interaction with an information space as a program. The program is then specialized to a user's known interests or information seeking activity by the technique of partial evaluation. In this paper, we elaborate PIPE by considering requirements analysis in the personalization lifecycle. We investigate the use of scenarios as a means of identifying and analyzing personalization requirements. As our first result, we show how designing a PIPE representation can be cast as a search within a space of PIPE models, organized along a partial order. This allows us to view the design of a personalization system, itself, as specialized interpretation of an information space. We then exploit the underlying equivalence of explanation-based generalization (EBG) and partial evaluation to realize high-level goals and needs identified in scenarios

Re-finding information is commonly cited as a problem on the Web. One reason re-finding on the Web is difficult is that while people rely on a considerable amount of context to return to information (e.g., the original path taken to it), the Web makes no guarantee that the context will remain static. The Re:Search Engine is designed to help people return to information in the dynamic environment of the Web by maintaining consistency in the search results it returns across time. For example, if Connie, while looking to purchase a Global Positioning System, found several systems she liked via a search for “GPS”, she would expect to be able to use the same query to locate the exact same systems again. However, simply returning the original result list when she re-issues the query might omit newly available GPS systems that she would like to see. The ideal result list would contain both the systems Connie remembers having seen and high quality new systems. Because people tend to remember little of what is presented in a result list, when a person repeats a query, the Re:Search Engine can preserve what is remembered about the original result set while still presenting new information.

searching facilities provide inadequate support to help them reach their information-seeking objectives. The emergence of interfaces with more advanced capabilities such as faceted browsing and result clustering can go some way to some way toward addressing such problems. The evaluation of these interfaces, however, is challenging since they generally offer diverse and versatile search environments that introduce overwhelming amounts of independent variables to user studies; choosing the interface object as the only independent variable in a study would reveal very little about why one design out-performs another. Nonetheless if we could effectively compare these interfaces we would have a way to determine which was best for a given scenario and begin to learn why. In this article we present a formative framework for the evaluation of advanced search interfaces through the quantification of the strengths and weaknesses of the interfaces in supporting user tactics and varying user conditions. This framework combines established models of users, user needs, and user behaviours to achieve this. The framework is applied to evaluate three search interfaces and demonstrates the potential value of this approach to interactive IR evaluation. † Primary Contact Author 1.

Searching for relevant information on the world-wide web is often a difficult and frustrating task. The information one is looking for, is hidden among thousands of documents returned by a search engine. One way of making search for relevant information easier, is to create better interfaces to the search engines

This thesis explores the possibilities of using the view-based search paradigm to create intelligent search interfaces on the Semantic Web. After surveying several current seman-tic search techniques, the view-based search paradigm is explained, and argued to fit in a valuable niche in the field. To test the argument, OntoViews, a semantic view-based search portal creation tool was designed and implemented, and eight portals with five vastly dif-ferent user interfaces were built using it. Based on the results of these experiments, this thesis argues that the paradigm, particularly as implemented in the OntoViews tool pro-vides a strong, extensible and flexible base on which to built semantic search applications. The particular problems faced in applying view-based search for semantic interfaces are noted, along with explanations on how they were solved in the OntoViews architecture. Finally, directions and ideas for future research are presented for both the paradigm and the implementation architecture, respectively.