Relational Markov models (RMMs) are a generalization of Markov models where states can be of different types, with each type described by a different set of variables. The domain of each variable can be hierarchically structured, and shrinkage is carried out over the cross product of these hierarchies. RMMs make effective learning possible in domains with very large and heterogeneous state spaces, given only sparse data. We apply them to modeling the behavior of web site users, improving prediction in our PROTEUS architecture for personalizing web sites. We present experiments on an e-commerce and an academic web site showing that RMMs are substantially more accurate than alternative methods, and make good predictions even when applied to previously-unvisited parts of the site.

Despite the connotation of the words "browsing" and "surfing," web usage often follows routine patterns of access. However, few mechanisms exist to assist users with these routine tasks; bookmarks or portal sites must be maintained manually and are insensitive to the user's browsing context. To fill this void, we designed and implemented the montage system. A web montage is an ensemble of links and content fused into a single view. Such a coalesced view can be presented to the user whenever he or she opens the browser or returns to the start page. We pose a number of hypotheses about how users would interact with such a system, and test these hypotheses with a fielded user study. Our findings support some design decisions, such as using browsing context to tailor the montage, raise questions about others, and point the way toward future work.

The fastest growing communityofweb users is that of mobile visitors who browse with wireless PDAs, cell phones, and pagers. Unfortunately,mostweb sites today are optimized exclusively for desktop, broadband clients, and deliver content poorly suited for mobile devices | devices that can display only a few lines of text, are on slow wireless network connections, and cannot run client-side programs or scripts. To best serve the needs of this growing community,wepropose building web site personalizers that observethebehavior of web visitors and automatically customize and adapt web sites for each individual mobile visitor. In this paper, welay the theoretical foundations for web site personalization, discuss our implementation of the web site personalizer #######, and present experiments evaluating its behavior on a number of academic and commercial web sites. Our initial results indicate that automatically adapting web content for mobile visitors saves a considerable amount of time and eort when seeking information \\on the go." Keywords Adaptiveweb sites, personalization, wireless web 1.

This paper describes a representation for pen-and-ink illustrations that allows the creation of high-fidelity illustrations at any scale or resolution.We represent a pen-and-ink illustration as a low-resolution grey-scale image, augmented by a set of discontinuity segments, along with a stroke texture.To render an illustration at a particular scale, we first rescale the grey-scale image to the desired size and then hatch the resulting image with pen-and-ink strokes.The main technical contribution of the paper is a new reconstruction algorithm that magnifies the low-resolution image while keeping the resulting image sharp along discontinuities.