Yahoo! Inc.

Sapan Panigrahi

Arun Kejariwal

Girish Vaitheeswaran

Paolo D'Alberto

Rosario Cammarota

Amir Behroozi

A system and method for identifying optimal system architectures for a reference application are provided. The system and method comprise executing a reference application and a plurality of test applications on a current system architecture and sampling performance data for each of the applications. The performance data is used to compute an application signature for each application. A similarity element is derived from the application signatures that illustrates the similarity between each application and every other application. Using a similarity threshold and an algorithm, a subset of test applications that are similar to the reference application are derived.

Method and system for predicting performance of software applications on prospective hardware architecture

A system for serving advertisements determines the frequency of occurrence for each parameter of a plurality of parameters associated with a plurality of search queries. The plurality of parameters are associated with one or more advertisements. The system stores at least some parameters to a first storage based on the frequency of occurrence of the parameters. The system stores the other parameters to a second storage that has a higher latency than the first storage. When serving advertisements, the system ranks advertisements for delivery based on the parameters stored in the first storage device and the second storage.

Fast networked based advertisement selection

The subject matter disclosed herein relates to alter an expression of executable instructions via a compiler component for use in ranking of electronic documents.

Compiler based code modification for use in document ranking

A computer readable medium stores a program for optimization for a search, and has sets of instructions for receiving a first decision tree. The first decision tree includes several nodes, and each node is for comparing a feature value to a threshold value. The instructions are for weighting the nodes within the first decision tree, determining the weighted frequency of a first feature within the first decision tree, and determining the weighted frequency of a second feature within the first decision tree. The instructions order the features based on the determined weighted frequencies, and store the ordering such that values of features having higher weighted frequencies are retrieved more often than values of features having lower weighted frequencies within the first decision tree.

Efficient data layout techniques for fast machine learning-based document ranking

The subject matter disclosed herein relates to prefetching data for use in ranking of electronic documents via a document ranking component.

Prefetching data for document ranking

Machine-learned ranking algorithms, e.g. for ranking search results, often use a sequence of decision trees involving decision nodes based on threshold values of features. Modules, systems and methods of optimizing such algorithms involve analyzing threshold feature values to determine threshold intervals for each feature and grouping decision trees according to the feature used in a root decision node. Then coalescing the decision trees within each group to form a coalesced group tree for each group and finally coalescing the coalesced group trees to form a coalesced tree that implements the algorithm.

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Castro Valley, CA, United States of America

Sapan Panigrahi