Modifying search result ranking based on implicit user feedback and a model of presentation bias
First Claim
1. A computer-implemented method comprising:
- obtaining information regarding selections of search results provided in response to a plurality of search queries, the obtained information for one or more of the selected search results comprising one or more presentation bias features of a presentation of the search result and one or more relevancy features of the search result, wherein at least one of the presentation bias features is a rank of the search result in the search results;
training a model using the obtained information, wherein the model is trained to predict a click through rate based on input comprising the one or more presentation bias features and the one or more relevancy features; and
providing the model for use with a search engine, wherein the search engine is configured to provide presentation bias and relevancy features of given search results as input to the model and to use predictive outputs of the model to reduce presentation bias in a presentation of the given search results by determining a quality score for each of the given search results and factoring out independent effects of presentation bias from the quality scores using the predictive outputs of the model, wherein the predictive outputs used to reduce the presentation bias in the presentation of the given search results include a predicted click through rate predicted based on the presentation bias and relevancy features of the given search results and the model.
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Accused Products
Abstract
The present disclosure includes systems and techniques relating to ranking search results of a search query. In general, the subject matter described in this specification can be embodied in a computer-implemented method that includes: receiving multiple features, including a first feature indicative of presentation bias that affects document result selection for search results presented in a user interface of a document search service; obtaining, based on the multiple features, information regarding document result selections for searches performed using the document search service; generating a prior model using the information, the prior model representing a background probability of document result selection given values of the multiple features; and outputting the prior model to a ranking engine for ranking of search results to reduce influence of the presentation bias.
285 Citations
27 Claims
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1. A computer-implemented method comprising:
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obtaining information regarding selections of search results provided in response to a plurality of search queries, the obtained information for one or more of the selected search results comprising one or more presentation bias features of a presentation of the search result and one or more relevancy features of the search result, wherein at least one of the presentation bias features is a rank of the search result in the search results; training a model using the obtained information, wherein the model is trained to predict a click through rate based on input comprising the one or more presentation bias features and the one or more relevancy features; and providing the model for use with a search engine, wherein the search engine is configured to provide presentation bias and relevancy features of given search results as input to the model and to use predictive outputs of the model to reduce presentation bias in a presentation of the given search results by determining a quality score for each of the given search results and factoring out independent effects of presentation bias from the quality scores using the predictive outputs of the model, wherein the predictive outputs used to reduce the presentation bias in the presentation of the given search results include a predicted click through rate predicted based on the presentation bias and relevancy features of the given search results and the model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system comprising:
data processing apparatus programmed to perform operations comprising; obtaining information regarding selections of search results provided in response to a plurality of search queries, the obtained information for one or more of the selected search results comprising one or more presentation bias features of a presentation of the search result and one or more relevancy features of the search result, wherein at least one of the presentation bias features is a rank of the search result in the search results; training a model using the obtained information, wherein the model is trained to predict a click through rate based on input comprising the one or more presentation bias features and the one or more relevancy features; and providing the model for use with a search engine, wherein the search engine is configured to provide presentation bias and relevancy features of given search results as input to the model and to use predictive outputs of the model to reduce presentation bias in a presentation of the given search results by determining a quality score for each of the given search results and factoring out independent effects of presentation bias from the quality scores using the predictive outputs of the model, wherein the predictive outputs used to reduce the presentation bias in the presentation of the given search results include a predicted click through rate predicted based on the presentation bias and relevancy features of the given search results and the model. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A machine-readable storage device having instructions stored thereon that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising:
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obtaining information regarding selections of search results provided in response to a plurality of search queries, the obtained information for one or more of the selected search results comprising one or more presentation bias features of a presentation of the search result and one or more relevancy features of the search result, wherein at least one of the presentation bias features is a rank of the search result in the search results; training a model using the obtained information, wherein the model is trained to predict a click through rate based on input comprising the one or more presentation bias features and the one or more relevancy features; and providing the model for use with a search engine, wherein the search engine is configured to provide presentation bias and relevancy features of given search results as input to the model and to use predictive outputs of the model to reduce presentation bias in a presentation of the given search results by determining a quality score for each of the given search results and factoring out independent effects of presentation bias from the quality scores using the predictive outputs of the model, wherein the predictive outputs used to reduce the presentation bias in the presentation of the given search results include a predicted click through rate predicted based on the presentation bias and relevancy features of the given search results and the model. - View Dependent Claims (20, 21, 22, 23, 24, 25, 26, 27)
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Specification