Enhanced Training Data for Learning-To-Rank
First Claim
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1. A method of producing training data for a learning-to-rank algorithm, the method comprising:
- obtaining {xm}m=1M corresponding to {dm}m=1M where x is a set of click-through data corresponding to a set of search results d;
modeling the training data in accordance with the following conditional probability function that indicates the probability of y given x;
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Abstract
Training data is used by learning-to-rank algorithms for formulating ranking algorithms. The training data can be initially provided by human judges, and then modeled in light of user click-through data to detect probable ranking errors. The probable ranking errors are provided to the original human judges, who can refine the training data in light of this information.
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Citations
20 Claims
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1. A method of producing training data for a learning-to-rank algorithm, the method comprising:
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obtaining {xm}m=1M corresponding to {dm}m=1M where x is a set of click-through data corresponding to a set of search results d; modeling the training data in accordance with the following conditional probability function that indicates the probability of y given x; - View Dependent Claims (2, 3, 4)
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5. A method of producing training data for a learning-to-rank algorithm, the method comprising:
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obtaining {xm}m=1M corresponding to {dm}m=1M where x is a set of click-through data corresponding to a set of search results d; modeling the training data in accordance with the following conditional probability function that indicates the probability of y given x; - View Dependent Claims (6, 7, 8)
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9. A method of producing training data for a learning-to-rank algorithm, the method comprising:
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modeling search results as having rankings according to relevance to a query; further modeling the ranking of any particular search result as depending on the relevance of search results other than the particular search result; calculating model parameters for the modeling based on (a) existing rankings of the search results and (b) click-through data corresponding to the search results; and calculating predicted rankings of the search results based on the modeling using the model parameters and the click-through data corresponding to the search results. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification