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Optimization framework for tuning ranking engine

  • US 8,108,374 B2
  • Filed: 09/16/2008
  • Issued: 01/31/2012
  • Est. Priority Date: 09/16/2008
  • Status: Active Grant
First Claim
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1. A method for facilitating the ranking of a plurality of web objects, comprising:

  • automatically adjusting a plurality of weight values for a plurality of parameters for inputting into a ranking engine that is adapted to rank a plurality of web objects based on such weight values and their corresponding parameters;

    providing the adjusted weight values to the ranking engine so as to generate a ranked set of web objects based on such adjusted weight values and their corresponding parameters, as well as a particular query;

    determining a relevance metric that quantifies or qualifies how relevant the generated ranked set of web objects are for the particular query; and

    automatically repeating the operations of adjusting the weight values, providing the adjusted weight values to the ranking engine, and determining a relevance metric until the relevance metric reaches an optimized level, which corresponds to an optimized set of weight values, and wherein the repeated operations utilize one or more sets of weight values including at least one set that results in a worst relevance metric value, as compared to a previous set of weight values, according to a certain probability in order to escape a local optimal solution to reach a global optimal solution,wherein the operation of automatically repeating the operations for adjusting the weights, providing the adjusted weight values, and determining a relevance metric is accomplished by;

    generating a N+1 vertex data structure, wherein N corresponds to the number of parameters for which weight values are to be adjusted; and

    transforming the data structure by a reflecting, expanding, and/or contracting operation on one or more vertices of the data structure whereby the transformed data structure'"'"'s resulting vertices correspond to the adjusted weight values that are provided to the ranking engine, and wherein the transforming of the data structure is dependent on a variable temperature, which decreases for one or more transformation operations based on a simulated annealing technique, so that the probability that the adjusted weight values results in a lower relevance metric is inversely proportional to a current temperature value of the variable temperature.

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