SYSTEM AND METHOD TO IDENTIFY CONTEXT-DEPENDENT TERM IMPORTANCE OF QUERIES FOR PREDICTING RELEVANT SEARCH ADVERTISEMENTS
First Claim
1. A computer system for predicting relevant search advertisements, comprising:
- a query term importance engine that applies a query term importance model for advertisement prediction that uses a plurality of term importance weights as a plurality of query features and a plurality of inverse document frequency weights of advertisement terms as a plurality of advertisement features to assign a relevance score to a plurality of sponsored advertisements;
a sponsored advertisement selection engine operably coupled to the query term importance engine that selects the plurality of sponsored advertisements scored by the query term importance engine that applies the query term importance model for advertisement prediction; and
a storage operably coupled to the sponsored advertisement selection engine that stores the query term importance model for advertisement prediction that uses the plurality of term importance weights as the plurality of query features and the plurality of inverse document frequency weights of advertisement terms as advertisement features to assign the relevance score to each of the plurality of sponsored advertisements.
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Abstract
An improved system and method for identifying context-dependent term importance of queries is provided. A query term importance model is learned using supervised learning of context-dependent term importance for queries and is then applied for advertisement prediction using term importance weights of query terms as query features. For instance, a query term importance model for query rewriting may predict rewritten queries that match a query with term importance weights assigned as query features. Or a query term importance model for advertisement prediction may predict relevant advertisements for a query with term importance weights assigned as query features. In an embodiment, a sponsored advertisement selection engine selects sponsored advertisements scored by a query term importance engine that applies a query term importance model using term importance weights as query features and inverse document frequency weights as advertisement features to assign a relevance score.
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Citations
20 Claims
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1. A computer system for predicting relevant search advertisements, comprising:
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a query term importance engine that applies a query term importance model for advertisement prediction that uses a plurality of term importance weights as a plurality of query features and a plurality of inverse document frequency weights of advertisement terms as a plurality of advertisement features to assign a relevance score to a plurality of sponsored advertisements; a sponsored advertisement selection engine operably coupled to the query term importance engine that selects the plurality of sponsored advertisements scored by the query term importance engine that applies the query term importance model for advertisement prediction; and a storage operably coupled to the sponsored advertisement selection engine that stores the query term importance model for advertisement prediction that uses the plurality of term importance weights as the plurality of query features and the plurality of inverse document frequency weights of advertisement terms as advertisement features to assign the relevance score to each of the plurality of sponsored advertisements. - View Dependent Claims (2, 3, 4)
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5. A computer-implemented method for predicting relevant search advertisements, comprising:
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assigning at least one term importance weight from a query term importance model as at least one query feature to a query; receiving a plurality of sponsored advertisements with inverse document frequency weights assigned as features to a plurality of terms for each sponsored advertisement; applying a term importance model for advertisement prediction that uses the at least one term importance weight term as the at least one query feature and a plurality of inverse document frequency weights of advertisement terms as advertisement features to assign a relevance score to each of the plurality of sponsored advertisements; assigning at least one sponsored advertisement of the plurality of sponsored advertisements assigned the relevance score to at least one web page placement in the sponsored advertisements area of the search results web page; and sending the at least one sponsored advertisement for display on the search results web page in a location of the at least one web page placement in the sponsored advertisement area of the search results web page. - View Dependent Claims (6, 7, 8, 9, 10, 11)
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12. A computer-readable storage medium having computer-executable instructions for performing the steps of:
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receiving a plurality of training sets of a training query and a training advertisement with a training relevance score; receiving a plurality of term importance weights for each training query in the plurality of training sets of the training query and the training advertisement with the training relevance score; assigning the plurality of term importance weights as a plurality of training query features to each training query in the plurality of training sets of the training query and the training advertisement with the training relevance score; training a model that uses the plurality of term importance weights as the plurality of training query features and a plurality of inverse document frequency weights of advertisement terms as training advertisement features for each of the plurality of training sets of the training query and the training advertisement to assign a prediction relevance score to each of the plurality of training sets of the training query and the training advertisement; and outputting the model to assign the prediction relevance score to a plurality of sets of a query and an advertisement using the plurality of term importance weights as a plurality of query features and the plurality of inverse document frequency weights of advertisement terms as advertisement features for each of the plurality of sets of the query and the advertisement. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20)
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Specification