Abstract: Techniques for using machine learning to leverage deep segment embeddings are provided. In one technique, a set of training data is processed using one or more machine learning techniques to train a neural network and learn an embedding for each segment of multiple segments. In response to receiving a request, multiple elements are identified, such as a source entity that is associated with the request, a source embedding for the source entity, a particular segment with which the source entity is associated, a segment embedding for the particular segment, and multiple target entities. For each target entity, a target embedding is identified and the target embedding, the source embedding, and the segment embedding are input into the neural network to generate output that is associated with the target entity. Based on the output, data about a subset of the target entities is presented on a computing device.

Abstract: Techniques for using machine learning to leverage deep segment embeddings are provided. In one technique, a set of training data is processed using one or more machine learning techniques to train a neural network and learn an embedding for each segment of multiple segments. In response to receiving a request, multiple elements are identified, such as a source entity that is associated with the request, a source embedding for the source entity, a particular segment with which the source entity is associated, a segment embedding for the particular segment, and multiple target entities. For each target entity, a target embedding is identified and the target embedding, the source embedding, and the segment embedding are input into the neural network to generate output that is associated with the target entity. Based on the output, data about a subset of the target entities is presented on a computing device.

Abstract: A method, system, and computer program product for of load balancing in a parallel map/reduce paradigm. The method commences by sampling a first set of input records, and forming a prospective load balancing assignment by assigning the first set of input records to the plurality of worker tasks based on a workload estimate for each of the worker tasks. To measure the prospective load balancing assignment, the method compares the workload variance over the plurality of worker tasks to a workload variance target, and also calculates a confidence level based on the sampled first set of input records. If the measured quality of the prospective load balancing assignment is not yet achieved, then the method samples additional input records; for example when the calculated workload variance is greater than the maximum workload variance target or when the calculated confidence level is lower than a confidence level threshold.

Abstract: A method, system, and computer program product for of load balancing in a parallel map/reduce paradigm. The method commences by sampling a first set of input records, and forming a prospective load balancing assignment by assigning the first set of input records to the plurality of worker tasks based on a workload estimate for each of the worker tasks. To measure the prospective load balancing assignment, the method compares the workload variance over the plurality of worker tasks to a workload variance target, and also calculates a confidence level based on the sampled first set of input records. If the measured quality of the prospective load balancing assignment is not yet achieved, then the method samples additional input records; for example when the calculated workload variance is greater than the maximum workload variance target or when the calculated confidence level is lower than a confidence level threshold.