Abstract: A system to reduce the number of factors that need to be considered in generating a prediction function includes an access module and a function generator module. The access module accesses a reduced set of factors derived from an original set of factors based at least in part on correlations between the factors of the original set. The function generator module generates, based on the reduced set of factors and a data set associated therewith, a plurality of potential prediction functions that operate on the data set to predict a result, evaluates performance of each one from the plurality of potential prediction functions, and selects a solution prediction function based on the evaluated.

Abstract: A method and a system to provide generate a search result ranking function, processing a search, and presenting search results are described to provide generate a search result ranking function, processing a search, and presenting search results are described. In one embodiment, a genetic algorithm module receives a plurality of factors, a test set of items, and an ordering solution representing the preferred ordering of the test set of items, generates a potential ranking function based on the plurality of factors, and apply the potential ranking function to each item in the test set of items to generate an ordering of items associated with the potential ranking function. The genetic algorithm module also compares the ordering of items with the ordering solution, and identifies, based on the comparison, the potential ranking function as a solution ranking function. A ranking function may assign a ranking score to items in a set of active items, the ranking score based on the solution ranking function.

Abstract: A method and a system to provide generate a search result ranking function, processing a search, and presenting search results are described to provide generate a search result ranking function, processing a search, and presenting search results are described. In one embodiment, a genetic algorithm module receives a plurality of factors, a test set of items, and an ordering solution representing the preferred ordering of the test set of items, generates a potential ranking function based on the plurality of factors, and apply the potential ranking function to each item in the test set of items to generate an ordering of items associated with the potential ranking function. The genetic algorithm module also compares the ordering of items with the ordering solution, and identifies, based on the comparison, the potential ranking function as a solution ranking function. A ranking function may assign a ranking score to items in a set of active items, the ranking score based on the solution ranking function.

Abstract: Methods and systems to reduce the number of factors or variables that need to be considered in generating a function such as a prediction function. The methods and systems may involve receiving a first set of factors and a data set associated with the first set of factors, calculating an importance value of each factor in the first set of factors, and selecting, based on the importance values, a second set of factors. In some embodiments, the methods and systems also include generating the prediction function based on the data set and the second set of factors.

Abstract: A method and a system to provide generate a search result ranking function, processing a search, and presenting search results are described to provide generate a search result ranking function, processing a search, and presenting search results are described. In one embodiment, a genetic algorithm module receives a plurality of factors, a test set of items, and an ordering solution representing the preferred ordering of the test set of items, generates a potential ranking function based on the plurality of factors, and apply the potential ranking function to each item in the test set of items to generate an ordering of items associated with the potential ranking function. The genetic algorithm module also compares the ordering of items with the ordering solution, and identifies, based on the comparison, the potential ranking function as a solution ranking function. A ranking function may assign a ranking score to items in a set of active items, the ranking score based on the solution ranking function.

Abstract: Methods and systems to reduce the number of factors or variables that need to be considered in generating a function such as a prediction function. The methods and systems may involve receiving a first set of factors and a data set associated with the first set of factors, calculating an importance value of each factor in the first set of factors, and selecting, based on the importance values, a second set of factors. In some embodiments, the methods and systems also include generating the prediction function based on the data set and the second set of factors.

Abstract: A method and a system to provide generate a search result ranking function, processing a search, and presenting search results are described to provide generate a search result ranking function, processing a search, and presenting search results are described. In one embodiment, a genetic algorithm module receives a plurality of factors, a test set of items, and an ordering solution representing the preferred ordering of the test set of items, generates a potential ranking function based on the plurality of factors, and apply the potential ranking function to each item in the test set of items to generate an ordering of items associated with the potential ranking function. The genetic algorithm module also compares the ordering of items with the ordering solution, and identifies, based on the comparison, the potential ranking function as a solution ranking function. A ranking function may assign a ranking score to items in a set of active items, the ranking score based on the solution ranking function.

Abstract: Methods and systems to reduce the number of factors or variables that need to be considered in generating a function such as a prediction function. The methods and systems may involve receiving a first set of factors and a data set associated with the first set of factors, calculating an importance value of each factor in the first set of factors, and selecting, based on the importance values, a second set of factors. In some embodiments, the methods and systems also include generating the prediction function based on the data set and the second set of factors.

Abstract: A distributed web crawler architecture is provided. An example system comprises a work items, a duplicate request detector, and a callback module. The work items monitor may be configured to detect a first work item from a first web crawler, the work item related to a URL. The duplicate request detector may be configured to determine that a second work item associated with the URL is present in a work queue, the work queue to provide work items to a fetcher, the second work item associated with a second web crawler The callback module may be configured to create a callback for the first web crawler, the callback indicating that a web page retrieved as a result of processing of the second work item is to be provided to the first web crawler, without queuing the first work item.