overhead required to navigate to it from scratch. We analysed 6 weeks of detailed usage data collected from 23 users of a wellknown web browser. We found that 58 % of an individual’s pages are revisits, and that users continually add new web pages into their repertoire of visited pages. People tend

of the user's interests, of the location and relevance of various items in the collection, and of the way in which others have interacted with the collection in the past. This paper describes a simple but operational tour guide, called Web-Watcher, which has given over 5000 tours to people browsing CMU

issues: (1) organizing WebWatcher to provide interactive advice to Mosaic users while logging their successful and unsuccessful searches as training data, and (2) incorporating machine learning methods to automatically acquire knowledge for selecting an appropriate hyperlink given the current web page

In studying actual Web searching by the public at large, we analyzed over one million Web queries by users of the Excite search engine. We found that most people use few search terms, few modified queries, view few Web pages, and rarely use advanced search features. A small number of search terms

The long-term success of the World Wide Web depends on fast response time. People use the Web to access information from remote sites, but do not like to wait long for their results. The latency of retrieving a Web document depends on several factors such as the network bandwidth, propagation

-use. This is accomplished in two ways. First, the system provides a unified index of information that a person has seen, whether it was seen as email, web page, document, appointment, etc. Second, because the information has been seen before, rich contextual cues can be used in the search interface. The system has been

The Web is a dynamic information environment. Web content changes regularly and people revisit Web pages frequently. But the tools used to access the Web, including browsers and search engines, do little to explicitly support these dynamics. In this paper we present DiffIE, a browser plug

We formulate and study search algorithms that consider a user’s prior interactions with a wide variety of content to personalize that user’s current Web search. Rather than relying on the unrealistic assumption that people will precisely specify their intent when searching, we pursue techniques

In this paper, we distill several years of our research on understanding and improving how people return to their previously visited web pages. Our motivation is that web page revisitation is one of the most frequent actions in computer use, and consequently any interface improvements

required to navigate to it from scratch. We analyzed 6 weeks of usage data collected from 23 users of a commercial browser. We found that 58?10of an individual’s pages are revisits, and that users continually add new web pages into their repertoire of visited pages. People tend to revisit pages just