Learning a Model of a Web User's Interests

Abstract. There are many recommender systems that are designed to help users find relevant information on the web. To produce recommendations that are rel- evant to an individual user, many of these systems first attempt to learn a model of the user's browsing behavior. This paper presents a novel method for learning such a model from a set of annotated web logs -- i.e., web logs that are aug- mented with the user's assessment of whether each webpage is an information content (IC) page (i.e., contains the information required to complete her task). Our systems use this to learn what properties of a webpage, within a sequence, identify such IC-pages, and similarly what "browsing properties" characterize the words on such pages ("IC-words"). As these methods deal with properties of webpages (or of words), rather than specific URLs (words), they can be used anywhere throughout the web; i.e., they are not specific to a particular website, or a particular task. This paper also describes the enhanced browser, A I E, that we designed and implemented for collecting these annotated web logs, and an em- pirical study we conducted to investigate the effectiveness of our approach. This empirical evidence shows that our approach, and our algorithms, work effectively.

Measuring and Understanding User Comfort With Resource Borrowing

Abstract. Resource borrowing is a common underlying approach in grid computing and thin-client computing. In both cases, external processes borrow resources that would otherwise be delivered to the interactive processes of end-users, creating contention that slows these processes and decreases the comfort of the end-users. How resource borrowing and user comfort are related is not well understood and thus resource borrowing tends to be extremely conservative. To address this lack of understanding, we have developed a sophisticated distributed application for directly measuring user comfort with the borrowing of CPU time, memory space, and disk bandwidth. Using this tool, we have conducted a controlled user study with qualitative and quantitative results that are of direct interest to the designers of grid and thin-client systems. We have found that resource borrowing can be quite aggressive without creating user discomfort, particularly in the case of memory and disk. We also describe an on-going Internet-wide study using our tool.

Learning Web Request Patterns

Summary. Most requests on the Web are made on behalf of human users, and like other human-computer interactions, the actions of the user can be characterized by identifiable regularities. Much of these patterns of activity, both within a user, and between users, can be identified and exploited by intelligent mechanisms for learn- ing Web request patterns. Our focus is on Markov-based probabilistic techniques, both for their predictive power and their popularity in Web modeling and other domains. Although history-based mechanisms can provide strong performance in predicting future requests, performance can be improved by including predictions from additional sources. In this chapter we review the common approaches to learning and predicting Web request patterns. We provide a consistent description of various algorithms (often independently proposed), and compare performance of those techniques on the same data sets. We also discuss concerns for accurate and realistic evaluation of these techniques.


注)このエントリーで紹介された論文は後にMatake's ISDL Reports : 2006にて詳細に紹介する予定です。


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