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We describe a new content publishing system that selects articles to serve to a user, choosing from an editorially programmed pool that is frequently refreshed. It is now deployed on a major Yahoo! portal, and selects articles to serve to hundreds of millions of user visits per day, significantly increasing the number of user clicks over the original manual(More)
Online content recommendation aims to identify trendy articles in a continuously changing dynamic content pool. Most of existing works rely on online user feedback, notably clicks, as the objective and maximize it by showing articles with highest click-through rates. Recently, click shaping was introduced to incorporate multiple objectives in a constrained(More)
We propose novel spatio-temporal models to estimate click-through rates in the context of content recommendation. We track article CTR at a fixed location over time through a dynamic Gamma-Poisson model and combine information from correlated locations through dynamic linear regressions, significantly improving on per-location model. Our models adjust for(More)
The ability to securely run arbitrary untrusted code on a wide variety of execution platforms is a challenging problem in the Grid community. One way to achieve this is to run the code inside a contained, isolated environment , namely a " sandbox ". Virtual machines provide a natural solution to the security and resource management issues that arise in(More)
Recommender problems with large and dynamic item pools are ubiquitous in web applications like content optimization, online advertising and web search. Despite the availability of rich item meta-data, excess heterogeneity at the item level often requires inclusion of item-specific "factors" (or weights) in the model. However, since estimating item factors(More)
Recommending interesting content to engage users is important for web portals (e.g. AOL, MSN, Yahoo!, and many others). Existing approaches typically recommend articles to optimize for a single objective, i.e., number of clicks. However a click is only the starting point of a user's journey and subsequent downstream utilities such as time-spent and revenue(More)
Digital Image inpainting methods provide a means for reconstruction of small damaged portions of an image. Image or video resources are often received in poor conditions, mostly with noise or defects making the resources difficult to read and understand. Some methods are presented that can be used for the reconstruction of damaged or partially known images.(More)