Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Generative Models For Item Adoptions Using Social Correlation, Freddy Chong Tat Chua, Hady Wirawan Lauw, Ee Peng Lim
Generative Models For Item Adoptions Using Social Correlation, Freddy Chong Tat Chua, Hady Wirawan Lauw, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Users face many choices on the Web when it comes to choosing which product to buy, which video to watch, etc. In making adoption decisions, users rely not only on their own preferences, but also on friends. We call the latter social correlation which may be caused by the homophily and social influence effects. In this paper, we focus on modeling social correlation on users’ item adoptions. Given a user-user social graph and an item-user adoption graph, our research seeks to answer the following questions: whether the items adopted by a user correlate to items adopted by her friends, and …
Based On Repeated Experience, System For Modification Of Expression And Negating Overload From Media And Optimizing Referential Efficiency, Peter R. Badovinatz, Veronika M. Megler
Based On Repeated Experience, System For Modification Of Expression And Negating Overload From Media And Optimizing Referential Efficiency, Peter R. Badovinatz, Veronika M. Megler
Computer Science Faculty Publications and Presentations
Content items are revealed to a user based on whether they have been previously reviewed by the user. A number of content items are thus received over time. The content items may be discrete content items, or may be portions of a content stream, and may be received over different media. For each content item, it is determined whether the content item was previously reviewed by a user. Where the content item was not previously reviewed, the item is revealed to the user, such as by being displayed or announced to the user. Where the content item was previously reviewed, …
Data Near Here: Bringing Relevant Data Closer To Scientists, Veronika M. Megler, David Maier
Data Near Here: Bringing Relevant Data Closer To Scientists, Veronika M. Megler, David Maier
Computer Science Faculty Publications and Presentations
Large scientific repositories run the risk of losing value as their holdings expand, if it means increased effort for a scientist to locate particular datasets of interest. We discuss the challenges that scientists face in locating relevant data, and present our work in applying Information Retrieval techniques to dataset search, as embodied in the Data Near Here application.
The Problem Of Semantics In The Metadata Mess, Veronika Margaret Megler, David Maier
The Problem Of Semantics In The Metadata Mess, Veronika Margaret Megler, David Maier
Computer Science Faculty Publications and Presentations
This presentation addresses problems related to the volume of available scientific data, and its accessibility or inaccessibility to researchers who seek it. Topics addressed include metadata and reducing semantic diversity, especially as they refer to geospatial and other architectures
Taming The Metadata Mess, Veronika Margaret Megler
Taming The Metadata Mess, Veronika Margaret Megler
Computer Science Faculty Publications and Presentations
The rapid growth of scientific data shows no sign of abating. This growth has led to a new problem: with so much scientific data at hand, stored in thousands of datasets, how can scientists find the datasets most relevant to their research interests? We have addressed this problem by adapting Information Retrieval techniques, developed for searching text documents, into the world of (primarily numeric) scientific data. We propose an approach that uses a blend of automated and “semi-curated” methods to extract metadata from large archives of scientific data, then evaluates ranked searches over this metadata. We describe a challenge identified …