SYSTEM AND METHOD FOR DEVELOPMENT OF SEARCH SUCCESS METRICS
First Claim
1. A method comprising the steps of:
- collecting, over a network, a plurality of search engine result pages;
determining, using at least one computing device, a target page success metric for each of the plurality of search engine result pages;
training, using the at least one computing device, a plurality of machine learned page success metrics using a first subset of the plurality of search engine result pages and each result page'"'"'s respective target page success metric, wherein each of the machine learned page success metrics is trained to predict the target page success metric for each of the first subset of the plurality of search engine result pages;
predicting, using the at least one computing device, using each of the plurality of machine learned page success metrics, a predicted target page success metric for each of a second subset of the search engine result pages;
evaluating, using the at least one computing device, the accuracy of each of the plurality of machine learned page success metrics in predicting the target page success metric associated with each of the second subset of search engine result pages; and
outputting, using the at least one computing device, a representation of the accuracy of the of each of the plurality of machine learned page success metrics to a display medium.
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Abstract
A system and method for development of search success metrics. A plurality of search engine result pages are collected and a target page success metric is determined for each page. A plurality of machine learned page success metrics are trained using a first subset of the search engine result pages and each result page'"'"'s respective target page success metric, wherein each of the machine learned page success metrics is trained to predict the target page success metric for each of the first subset of search engine result pages. A predicted target page success metric is predicted for each of a second subset of the search engine result pages using each of the machine learned page success metrics. The accuracy of each of the machine learned page success metrics in predicting the target page success metric associated with each of the second subset of search engine result pages is then evaluated.
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Citations
30 Claims
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1. A method comprising the steps of:
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collecting, over a network, a plurality of search engine result pages; determining, using at least one computing device, a target page success metric for each of the plurality of search engine result pages; training, using the at least one computing device, a plurality of machine learned page success metrics using a first subset of the plurality of search engine result pages and each result page'"'"'s respective target page success metric, wherein each of the machine learned page success metrics is trained to predict the target page success metric for each of the first subset of the plurality of search engine result pages; predicting, using the at least one computing device, using each of the plurality of machine learned page success metrics, a predicted target page success metric for each of a second subset of the search engine result pages; evaluating, using the at least one computing device, the accuracy of each of the plurality of machine learned page success metrics in predicting the target page success metric associated with each of the second subset of search engine result pages; and outputting, using the at least one computing device, a representation of the accuracy of the of each of the plurality of machine learned page success metrics to a display medium. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19)
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15. The method of claim 15 wherein the generalized discounted cumulative gain metric is determined as follows,
for a set of search result pages {k} where wr=rank weight for result at rank r wdr=relevance weight for result at rank r the generalized cumulative gain metric (GDCG) is a function of wr and wdr wherein the values of wr and wdr are varied such that the function Λ - is minimized, where
Λ
=Σ
k (GDCGk−
Sk)2where k is the kth page view and S is the target page success metric.
- is minimized, where
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20. A system comprising:
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a search engine result pages collection module comprising one or more processors programmed to execute software code retrieved from a computer readable storage medium storing software for collecting, over a network, a plurality of search engine result pages; a target page success metrics determination module comprising one or more processors programmed to execute software code retrieved from a computer readable storage medium storing software for determining a target page success metric for each of the plurality of search engine result pages; a machine learned page success metrics training comprising one or more processors programmed to execute software code retrieved from a computer readable storage medium storing software for training a plurality of machine learned page success metrics using a first subset of the plurality of search engine result pages and each result page'"'"'s respective target page success metric, wherein each of the machine learned page success metrics is trained to predict the target page success metric for each of the first subset of the plurality of search engine result pages; a target page success prediction module comprising one or more processors programmed to execute software code retrieved from a computer readable storage medium storing software for predicting, using each of the plurality of machine learned page success metrics, a predicted target page success metric for each of a second subset of the search engine result pages; and a page success metrics evaluation module comprising one or more processors programmed to execute software code retrieved from a computer readable storage medium storing software for evaluating the accuracy of each of the plurality of plurality of machine learned page success metrics in predicting the target page success metric associated with each of the second subset of search engine result pages. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29)
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30. A computer-readable medium having computer-executable instructions for a method comprising the steps of:
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collecting, over a network, a plurality of search engine result pages; determining, using at least one computing device, a target page success metric for each of the plurality of search engine result pages; training, using the at least one computing device, a plurality of machine learned page success metrics using a first subset of the plurality of search engine result pages and each result page'"'"'s respective target page success metric, wherein each of the machine learned page success metrics is trained to predict the target page success metric for each of the first subset of the plurality of search engine result pages; predicting, using the at least one computing device, using each of the plurality of machine learned page success metrics, a predicted target page success metric for each of a second subset of the search engine result pages; evaluating, using the at least one computing device, the accuracy of each of the plurality of machine learned page success metrics in predicting the target page success metric associated with each of the second subset of search engine result pages; and outputting, using the at least one computing device, a representation of the accuracy of the of each of the plurality of machine learned page success metrics to a display medium.
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Specification