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Search result ranking using machine learning

  • US 8,924,314 B2
  • Filed: 08/04/2011
  • Issued: 12/30/2014
  • Est. Priority Date: 09/28/2010
  • Status: Active Grant
First Claim
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1. A method comprising:

  • implementing a goal model for a first goal from a plurality of goals using machine learning, the goal model being at least one of a neural network or an expert system;

    factorizing raw data to a set of data factors for the first goal, the raw data including query data from a user search query and at least one of relevance of an item title, temporal data, transaction data, impressions of item listings, item demand, or item supply;

    assigning a plurality of impact scores to each of the set of data factors, a first impact score of the plurality of impact scores corresponding to the first goal and a second impact score of the plurality of impact scores corresponding to a second goal in the plurality of goals, the plurality of impact scores respectively measuring the degree to which changes in a data factor influence a corresponding model'"'"'s output, the first impact score corresponding to revenue generation and the second impact score corresponding to cross product marketing;

    ranking the set of data factors based on the first impact score;

    selecting a plurality of data factors from the set of data factors based on the ranking, the plurality of data factors being a proper subset of the set of data factors;

    modifying the values of the plurality of data factors by respective weights prior to being inputted into the goal model;

    inputting, responsive to the search query, the plurality of data factors into the goal model to;

    select search results from a database using the goal model; and

    create a model output, the model output including a ranking for each result in the search results; and

    presenting, to a user, an ordered list of search results based on the ranking for each result in the search results from the model output.

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