Abstract: Methods and systems for ranking a plurality of articles for rendering on a website for a user account include receiving a request for accessing the website. Features are identified for the plurality of articles selected for rendering on the website. Each feature is associated with a value parameter having a value in a multi-dimensional vector space. A pair of solutions is identified for an article of the plurality of articles, wherein the pair of solutions identifies a portion of the multi-dimensional vector space that satisfies multiple objectives. A vector point defining the optimal solution is selected for the article from within the portion of the multi-dimensional vector space. The selected vector point is used in computing an article score for the article. The article score for the plurality of articles is used to identify and present a subset of the articles on the website for the user.

Abstract: Methods and systems for ranking a plurality of articles for rendering on a website for a user account include receiving a request for accessing the website. Features are identified for the plurality of articles selected for rendering on the website. Each feature is associated with a value parameter having a value in a multi-dimensional vector space. A pair of solutions is identified for an article of the plurality of articles, wherein the pair of solutions identifies a portion of the multi-dimensional vector space that satisfies multiple objectives. A vector point defining the optimal solution is selected for the article from within the portion of the multi-dimensional vector space. The selected vector point is used in computing an article score for the article. The article score for the plurality of articles is used to identify and present a subset of the articles on the website for the user.

Abstract: Described is a system for assessing the quality of machine learning algorithms over massive time series. A set of random blocks of a time series data sample of size n is selected in parallel. Then, r resamples are generated, in parallel, by applying a bootstrapping method to each block in the set of random blocks to obtain a resample of size n, where r is not fixed. Errors are estimated on the r resamples, and a final accuracy estimate is produced by averaging the errors estimated on the r resamples.