Oath Inc.

The instant system and methods solves the cold start problem through various systems and methods directed to aggregating user interaction data associated with a user over a period of time, scoring the user interaction data to determine at least one user interest relevance score and/or at least one surfacing user interest score for each of the plurality of user interaction types, wherein the scoring includes a time sensitive weighting scheme, and generating a user interest profile partition for each of the plurality of user interaction types based on the at least one user interest relevance score and/or the at least one surfacing user interest score.

One or more computing devices, systems, and/or methods for classifier validation are provided. A set of in-sample examples are partitioned into a reduced in-sample set and a remaining in-sample set. The reduced in-sample set is processed using a set of classifiers. A subset of classifiers are identified as having error counts, over the reduced in-sample set, below a threshold number of errors. A training procedure is executed to select a classifier having a minimum error rate over the set of in-sample examples. If the classifier is within the subset of classifiers, then an out-of-sample error bound is determined for the classifier.

One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.

One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with a sparse set of field weights associated with feature fields associated with features of the plurality of sets of auction information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. Positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon field weights, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the positive signal probabilities.

One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with client devices and entities. A network profile associated with the client devices and the entities may be generated based upon the event information. A similarity profile associated with the client devices may be generated based upon the network profile. The similarity profile may be indicative of one or more similarity scores associated with a first client device and one or more client devices. A user profile associated with the first client device may be modified, based upon the similarity profile and/or one or more user profiles associated with the one or more client devices, to generate a modified user profile. Content may be selected for presentation via the first client device based upon the modified user profile.

The present teaching relates to method, system, medium, and implementations for machine learning. Machine learning is performed based on training data via a dual loop learning process that includes a first loop for data decoding learning and a second loop for label decoding learning. In the first loop, first parameters associated with decoding are updated to generate updated first parameters based on a first label, estimated via the decoding using the first parameters, and a second label, predicted via the label decoding using second parameters. In the second loop, the second parameters associated with the label decoding are updated to generate updated second parameters based on a third label, obtained via the decoding using the updated first parameters, and a ground truth label.

Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of impression indications associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine win probabilities and/or expected bid surpluses associated with multiple shaded bid values based upon one or more feature parameters, of the feature parameters, associated with the second set of features. A shaded bid value for submission may be determined based upon the win probabilities and/or the expected bid surpluses.

One or more computing devices, systems, and/or methods are provided. Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of minimum bid values to win associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, unshaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using a loss function and/or the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine a shaded bid value for submission based upon one or more first feature parameters, of the feature parameters, associated with the second set of features.

The present teaching relates to method, system, medium, and implementations for machine learning. A training sample is sent to an expert for training a model representative of the expert. A prediction is received, which is generated by the expert in accordance with the training sample and based on one or more parameters associated with the model. A metric with respect to the prediction characterizing the prediction received from the expert is analyzed. When the metric satisfies a first criterion, a ground truth label associated with the training sample is sent to the expert to facilitate the training.

The present teaching relates to method, system, medium, and implementations for machine learning. A check is performed on a level of available bidding currency for bidding a training sample that is used to train a model via machine learning. A bid in an amount within the available bidding currency is sent, to a source of the training sample, for the training sample. The training sample is received from the source when the bid is successful. A prediction is then generated in accordance with the training sample based on one or more parameters associated with the model and is sent to the source.