Pruning field weights for content selection

A computing device comprising:

• a processor;
• and
• memory comprising processor-executable instructions that when executed by the processor cause performance of operations, the operations comprising:
• performing one or more pruning operations to generate a first machine learning model with a sparse set of field weights associated with feature fields associated with features of a plurality of sets of information, wherein each field weight of the sparse set of field weights is associated with two feature fields;
• receiving a request for content associated with a client device;
• determining, based upon the request for content, a set of features associated with the request for content, wherein:
• the set of features comprises a first feature associated with a first feature field and a second feature associated with a second feature field;
• and
• a first field weight, associated with the first feature field and the second feature field, is pruned via the one or more pruning operations;
• determining, using the first machine learning model, a plurality of positive signal probabilities associated with a plurality of content items based upon one or more first field weights, of the first machine learning model, associated with the set of features, wherein a first positive signal probability of the plurality of positive signal probabilities is associated with a content item of the plurality of content items and corresponds to a probability of receiving a positive signal responsive to presenting the content item via the client device;
• selecting, from the plurality of content items, the content item for presentation via the client device based upon the plurality of positive signal probabilities;
• and
• transmitting the content item to the client device.

A non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising:

• performing one or more pruning operations to generate a first machine learning model with a sparse set of field weights associated with feature fields associated with features of a plurality of sets of auction information, wherein each field weight of the sparse set of field weights is associated with two feature fields;
• receiving a bid request, wherein the bid request is associated with a request for content associated with a client device;
• determining, based upon the bid request, a set of features associated with the request for content, wherein:
• the set of features comprises a first feature associated with a first feature field and a second feature associated with a second feature field;
• and
• a first field weight, associated with the first feature field and the second feature field, is pruned via the one or more pruning operations;
• determining, using the first machine learning model, a plurality of click probabilities associated with a plurality of content items based upon one or more first field weights, of the first machine learning model, associated with the set of features, wherein a first click probability of the plurality of click probabilities is associated with a content item of the plurality of content items and corresponds to a probability of receiving a selection of the content item responsive to presenting the content item via the client device;
• selecting, from the plurality of content items, the content item for presentation via the client device based upon the plurality of click probabilities;
• and
• submitting a bid value associated with the content item to an auction module for participation in an auction associated with the request for content.