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Full-Text Articles in Computer Sciences
Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao
Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao
Computer Science Faculty Publications
Clustering is well-suited for Web mining by automatically organizing Web pages into categories, each of which contains Web pages having similar contents. However, one problem in clustering is the lack of general methods to automatically determine the number of categories or clusters. For the Web domain in particular, currently there is no such method suitable for Web page clustering. In an attempt to address this problem, we discover a constant factor that characterizes the Web domain, based on which we propose a new method for automatically determining the number of clusters in Web page data sets. We discover that the …
Personalization By Program Slicing, Saverio Perugini, Naren Ramakrishnan
Personalization By Program Slicing, Saverio Perugini, Naren Ramakrishnan
Computer Science Faculty Publications
Personalization involves customizing information access to the end-user. As any new area of computer science research it lacks formal models to guide the design of systems. In this paper, we present a modeling methodology, based on generative programming, for personalizing interactions with hierarchical websites. The methodology entails modeling a user’s interaction with a site in a program and applying program slicing to personalize the interaction. While preserving interactivity, this approach does not require the designer to anticipate all possible user interactions a priori and provide interfaces for each. Moreover, it provides a theoretical, systematic, and implementation-neutral way to design systems …
A Generative Programming Approach To Interactive Information Retrieval: Insights And Experiences, Saverio Perugini, Naren Ramakrishnan
A Generative Programming Approach To Interactive Information Retrieval: Insights And Experiences, Saverio Perugini, Naren Ramakrishnan
Computer Science Faculty Publications
We describe the application of generative programming to a problem in interactive information retrieval. The particular interactive information retrieval problem we study is the support for "out-of-turn interaction" with a website – how a user can communicate input to a website when the site is not soliciting such information on the current page, but will do so on a subsequent page. Our solution approach makes generous use of program transformations (partial evaluation, currying, and slicing) to delay the site’s current solicitation for input until after the user’s out-of-turn input is processed. We illustrate how studying out-of-turn interaction through a generative …
The Good, Bad And The Indifferent: Explorations In Recommender System Health, Benjamin J. Keller, Sun-Mi Kim, N. Srinivas Vemuri, Naren Ramakrishnan, Saverio Perugini
The Good, Bad And The Indifferent: Explorations In Recommender System Health, Benjamin J. Keller, Sun-Mi Kim, N. Srinivas Vemuri, Naren Ramakrishnan, Saverio Perugini
Computer Science Faculty Publications
Our work is based on the premise that analysis of the connections exploited by a recommender algorithm can provide insight into the algorithm that could be useful to predict its performance in a fielded system. We use the jumping connections model defined by Mirza et al. [6], which describes the recommendation process in terms of graphs. Here we discuss our work that has come out of trying to understand algorithm behavior in terms of these graphs. We start by describing a natural extension of the jumping connections model of Mirza et al., and then discuss observations that have come from …