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It is quite common for web sites to allow users to customize the site for themselves. Common manual customizations include lists of favorite links, stock quotes of interest, and local weather reports. Slightly automated customizations include records of previous interactions with the site and references to pages that have changed since the previous visit. Some sites also allow users to describe interests and will present information -- news articles, for example -- relevant to those interests.

More sophisticated sites attempt path prediction: guessing where the user wants to go and taking her there immediately (or at least providing a link). The WebWatcher [5][*] learns to predict what links users will follow on a particular page as a function of their specified interests. A link that WebWatcher believes a particular user is likely to follow will be highlighted graphically and duplicated at the top of the page when it is presented. Visitors to a site are asked, in broad terms, what they are looking for. Before they depart, they are asked if they have found what they wanted. WebWatcher takes an access-based approach, using the paths of people who indicated success as examples of successful navigations. If, for example, many people who were looking for ``personal home pages'' follow the ``people'' link, then WebWatcher will tend to highlight that link for future visitors with the same goal. Note that, because WebWatcher groups people based on their stated interests rather than customizing to each individual, it falls on the continuum between pure customization and pure transformation.

A site may also try to customize to a user by trying to guess her general interests dynamically as she browses. The AVANTI Project [7][*] focuses on dynamic customization based on users' needs and tastes. As with the WebWatcher, AVANTI relies partly on users providing information about themselves when they enter the site. Based on what it knows about the user, AVANTI attempts to predict both the user's eventual goal and her likely next step. AVANTI will prominently present links leading directly to pages it thinks a user will want to see. Additionally, AVANTI will highlight links that accord with the user's interests.

Another form of customization is based on collaborative filtering. In collaborative filtering, users rate objects (e.g. web pages or movies) based on how much they like them. Users that tend to give similar ratings to similar objects are presumed to have similar tastes; when a user seeks recommendations of new objects, the site suggests those objects that were highly rated by other users with similar tastes. The site recommends objects based solely on other users' ratings or accesses, ignoring the content of the objects themselves. A simple form of collaborative filtering is used by, for example, Amazon.com; the web page for a particular book may have links to other books commonly purchased by people who bought this one. Firefly[*] uses a more individualized form of collaborative filtering in which members may rate hundreds of CDs or movies, building up a very detailed personal profile; Firefly then compares this profile with those of other members to make new recommendations.

Footprints [23] takes an access-based transformation approach. Their motivating metaphor is that of travelers creating footpaths in the grass over time. Visitors to a web site leave their ``footprints'' behind, in the form of counts of how often each link is traversed; over time, ``paths'' accumulate in the most heavily traveled areas. New visitors to the site can use these well-worn paths as indicators of the most interesting pages to visit. Footprints are left automatically (and anonymously), and any visitor to the site may see them; visitors need not provide any information about themselves in order to take advantage of the system. Footprints provides essentially localized information; the user sees only how often links between adjacent pages are traveled.

A web site's ability to adapt could be enhanced by providing it with meta-information: information about its content, structure, and organization. One way to provide meta-information is to represent the site's content in a formal framework with precisely defined semantics, such as a database or a semantic network. The use of meta-information to customize or optimize web sites has been explored in a number of projects (see, for example, XML annotations [9], Apple's Meta-Content Format, and other projects [6,11]). One example of this approach is the STRUDEL web-site management system [6] which attempts to separate the information available at a web site from its graphical presentation. Instead of manipulating web sites at the level of pages and links, web sites may be specified using STRUDEL's view-definition language. With all of the site's content so encoded, its presentation may be easily adapted.

A number of projects have explored client-side customization, in which a user has her own associated agent who learns about her interests and customizes her web experience accordingly. The AiA project [4,17] explores the customization of web page information by adding a ``presentation agent'' who can direct the user's attention to topics of interest. The agent has a model of the individual user's needs, preferences, and interests and uses this model to decide what information to highlight and how to present it. In the AiA model, the presentation agent is on the client side, but similar techniques could be applied to customized presentation by a web server as well. Letizia [10] is a personal agent that learns a model of its user by observing her behavior. Letizia explores the web ahead of the user (investigating links off of the current page) and uses its user model to recommend pages it thinks the user will enjoy. Other projects have investigated performing customization at neither the client nor the server but as part of the network in between, particularly by using transcoding proxies. Transend [8], for example, is a proxy server at the University of California at Berkeley that performs image compression and allows each of thousands of users to customize the degree of compression, the interface for image refinement, and the web pages to which compression is applied.


next up previous
Next: Our Approach Up: Introduction and Motivation Previous: Design Space
Mike Perkowitz
1999-03-02