Abstract: Embodiments of the disclosed technologies receive a first-party trained model and a first-party data set from a first-party system into a protected environment, receive a first third-party data set into the protected environment, and, in a data clean room, joining the first-party data set and the first third-party data set to create a joint data set for the particular segment, tuning a first-party trained model with the joint data set to create a third-party tuned model, sending model parameter data learned in the data clean room as a result of the tuning to an aggregator node, receiving a globally tuned version of the first-party trained model from the aggregator node, applying the globally tuned version of the first-party trained model to a second third-party data set to produce a scored third-party data set, and providing the scored third-party data set to a content distribution service of the first-party system.

Abstract: Techniques are provided for automatically tuning a parameter in a layered model framework. One or more machine learning techniques are used to train multiple versions of a first model that includes a first version and a second version. A second model is stored that includes a parameter and accepts, as input, output from the first model. Multiple parameter values of the parameter are tested when processing content requests using the first and second versions of the first model. A strict subset of the plurality of parameter values are selected for the parameter of the second model, such that processing a first subset of the content requests using the first version of the first model results in a first value of a particular metric that matches a second value of the particular metric resulting from processing a second subset of the content requests using the second version of the first model.

Abstract: Techniques for modifying training data for video response quality optimization are provided. In one technique, training data is identified that is generated based on video presentation data that indicates multiple video items were presented to multiple entities. The training data comprises multiple training instances, each indicating a presentation of at least a portion of a video item to an entity. For each training instance in a subset of the training instances, a quality metric of the presentation of the video item indicated in said each training instance is computed and that training instance is modified based on the quality metric. After modifying one or more of the training instances, the model is trained using one or more machine learning techniques. In response to a content request, the model is used to determine whether to transmit a particular video item over a network to a computing device of a particular entity.

Abstract: Techniques for modifying training data for video response quality optimization are provided. In one technique, training data is identified that is generated based on video presentation data that indicates multiple video items were presented to multiple entities. The training data comprises multiple training instances, each indicating a presentation of at least a portion of a video item to an entity. For each training instance in a subset of the training instances, a quality metric of the presentation of the video item indicated in said each training instance is computed and that training instance is modified based on the quality metric. After modifying one or more of the training instances, the model is trained using one or more machine learning techniques. In response to a content request, the model is used to determine whether to transmit a particular video item over a network to a computing device of a particular entity.

Abstract: Techniques are provided for automatically tuning a parameter in a layered model framework. One or more machine learning techniques are used to train multiple versions of a first model that includes a first version and a second version. A second model is stored that includes a parameter and accepts, as input, output from the first model. Multiple parameter values of the parameter are tested when processing content requests using the first and second versions of the first model. A strict subset of the plurality of parameter values are selected for the parameter of the second model, such that processing a first subset of the content requests using the first version of the first model results in a first value of a particular metric that matches a second value of the particular metric resulting from processing a second subset of the content requests using the second version of the first model.

Abstract: Techniques for using attention events for audience expansion are provided. In one technique, first interaction data that indicates multiple interactions by the first entity with multiple content items is stored. The interactions includes an interaction that is based on an amount of time that content within one of the content items was presented to the first entity. Based on the first interaction data, similarity data that identifies one or more content delivery campaigns that are similar to a particular content delivery campaign is generated. Second interaction data that indicates interaction(s) by a second entity with content item(s) is stored. Based on the second interaction data and the similarity data, association data that associates the second entity with the particular content delivery campaign is stored. The association data may be used to identify the particular campaign in response to receiving a content request from a computing device of the second entity.

Abstract: During operation, a system obtains member features associated with a member of a network, wherein the set of member features include a job-seeking status of the member. Next, the system analyzes the member features to predict an interest of the member in career services associated. The system then uses the predicted interest to output a recommendation of the career services to the member.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system identifies a set of providers that meet a set of requirements in a request for proposal (RFP) from a consumer based on an overall compatibility between the set of requirements and the set of providers. Next, the system generates a ranking of the set of providers based on the compatibility and a network distance between the consumer and the set of providers in a social network. The system then selects one or more providers from the ranking as matches for the RFP. Finally, the system transmits the RFP to the one or more providers.