Information filtering agents and collaborative filtering both attempt to alleviate information overload by identifying which items a user will find worthwhile. Information filtering (IF) focuses on the analysis of item content and the development of a personal user interest profile. Collaborative filtering (CF) focuses on identification of other users with similar tastes and the use of their opinions to recommend items. Each technique has advantages and limitations that suggest that the two could be beneficially combined. This paper shows that a CF framework can be used to combine personal IF agents and the opinions of a community of users to produce better recommendations than either agents or users can produce alone. It also shows that using CF to create a personal combination of a set of agents produces better results than either individual agents or other combination mechanisms. One key implication of these results is that users can avoid having to select among ag...

This paper describes the wearable Remembrance Agent, a continuously running proactive memory aid that uses the physical context of a wearable computer to provide notes that might be relevant in that context. A currently running prototype is described, along with future directions for research inspired by using the prototype. 1 Introduction With computer chips getting smaller and cheaper the day will soon come when the desk-top, lap-top, and palm-top computer will all disappear into a vest pocket, wallet, shoe, or anywhere else a spare centimeter or two are available. As the price continues to plummet, these devices will enable all kinds of applications, from consumer electronics to personal communicators to field-operations support. Given that the primary use of today's palm-top computers is as day-planners, address books, and notebooks, one can expect memory aids will be an important application for wearable computers as well. Current computer-based memory aids are written to make l...

Recommender systems provide personalized suggestions about items that users will find interesting. Typically, recommender systems require a user interface that can "intelligently" determine the interest of a user and use this information to make suggestions. The common solution, "explicit ratings", where users tell the system what they think about a piece of information, is well-understood and fairly precise. However, having to stop to enter explicit ratings can alter normal patterns of browsing and reading. A more "intelligent " method is to use implicit ratings, where a rating is obtained by a method other than obtaining it directly from the user. These implicit interest indicators have obvious advantages, including removing the cost of the user rating, and that every user interaction with the system can contribute to an implicit rating. Current recommender systems mostly do not use implicit ratings, nor is the ability of implicit ratings to predict actual user interest well-understood. This research studies the correlation between various implicit ratings and the explicit rating for a single Web page. A Web browser was developed to record the user's actions (implicit ratings) and the explicit rating of a page. Actions included mouse clicks, mouse movement, scrolling and elapsed time. This browser was used by over 80 people that browsed more than 2500 Web pages. Using the data collected by the browser, the individual implicit ratings and some combinations of implicit ratings were analyzed and compared with the explicit rating. We found that the time spent on a page, the amount of scrolling on a page and the combination of time and scrolling had a strong correlation with explicit interest, while individual scrolling methods and mouse-clicks were ineffective in predicting explicit interest. 1

Most web pages are linked to others with related content. This idea, combined with another that says that text in, and possibly around, HTML anchors describe the pages to which they point, is the foundation for a usable WorldWide Web. In this paper, we examine to what extent these ideas hold by empirically testing whether topical locality mirrors spatial locality of pages on the Web. In particular, we find that the likelihood of linked pages having similar textual content to be high; the similarity of sibling pages increases when the links from the parent are close together; titles, descriptions, and anchor text represent at least part of the target page; and that anchor text may be a useful discriminator among unseen child pages. These results show the foundations necessary for the success of many web systems, including search engines, focused crawlers, linkage analyzers, and intelligent web agents.

User-interruption in human-computer interaction (HCI) is an increasingly important problem. Many of the useful advances in intelligent and multitasking computer systems have the significant side effect of greatly increasing user-interruption. This previously innocuous HCI problem has become critical to the successful function of many kinds of modern computer systems. Unfortunately, no HCI design guidelines exist for solving this problem. In fact, theoretical tools do not yet exist for investigating the HCI problem of user-interruption in a comprehensive and generalizable way. This report asserts that a single unifying definition of user-interruption and the accompanying practical taxonomy would be useful theoretical tools for driving effective investigation of this crucial HCI problem. These theoretical tools are constructed here. A comprehensive analysis is conducted through the existing literature. Theoretical constructs from several relevant but diverse fields are identified and discussed. A unifying definition of user-interruption is synthesized. This new definition is supported with an array of postulates, assertions, and a taxonomy of human interruption to facilitate its practical application.

Peripheral information is information that is not central to a person's current task, but provides the person the opportunity to learn more, to do a better job, or to keep track of less important tasks. Though peripheral information displays are ubiquitous, they have been rarely studied. For computer users, a common peripheral display is a scrolling text display that provides announcements, sports scores, stock prices, or other news. In this paper, we investigate how to design peripheral displays so that they provide the most information while having the least impact on the user's performance on the main task. We report a series of experiments on scrolling displays aimed at examining tradeoffs between distraction of scrolling motion and memorability of information displayed. Overall, we found that continuously scrolling displays are more distracting than displays that start and stop, but information in both is remembered equally well. These results are summarized in a set of design recommendations.

The Apt Decision agent learns user preferences in the domain of rental real estate by observing the user's critique of apartment features. Users provide a small number of criteria in the initial interaction, receive a display of sample apartments, and then react to any feature of any apartment independently, in any order. Users learn which features are important to them as they discover the details of specific apartments. The agent uses interactive learning techniques to build a profile of user preferences, which can then be saved and used in further retrievals. Because the user's actions in specifying preferences are also used by the agent to create a profile, the result is an agent that builds a profile without redundant or unnecessary effort on the user's part.

The Internet and World Wide Web have brought us into a world of endless possibilities: interactive Web sites to experience, music to listen to, conversations to participate in, and every conceivable consumer item to order. But this world also is one of endless choice: how can we select from a huge universe of items of widely varying quality? Computational recommender systems have emerged to address this issue. They enable people to share their opinions and benefit from each other's experience. We present a framework for understanding recommender systems and survey a number of distinct approaches in terms of this framework. We also suggest two main research challenges: (1) helping people form communities of interest while respecting personal privacy, and (2) developing algorithms that combine multiple types of information to compute recommendations. In HCI In The New Millennium, Jack Carroll, ed., Addison-Wesley, 2001 p. 2 of 21 Introduction The new millennium is an age of i...

Technical, cognitive, and social factors inhibit the widespread success of systematic software reuse. Our research is primarily concerned with the cognitive and social challenges faced by software developers: how to motivate them to reuse and how to reduce the difficulty of locating components from a large reuse repository. Our research has explored a new interaction style between software developers and reuse repository systems enabled by information delivery mechanisms. Instead of passively waiting for software developers to explore the reuse repository with explicit queries, information delivery autonomously locates and presents components by using the developers' partially written programs as implicit queries. We have designed

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