Microsoft Technology Licensing, LLC

Oracle International Corporation

Smriti R Ramakrishnan

Garret Frederick Swart

Alan Paul Wood

Aastha Jain

Parag Agrawal

Suratna Budalakoti

Yao Chen

Ashish Jain

Lingjie Weng

Jim Chang

Arvind Murali Mohan

Dipen Thakkar

Hongbo Zhao

Shifu Wang

Xiaoxi Zhao

Daniel D Thorndyke

Lu Chen

In an example embodiment, machine learning is utilized to make recommendations for next actions by users of an online network. These next actions are called 'next best actions.' The machine learning may be performed to train a multitask deep machine learning model to make recommendations based on a series of inputs, including, for example, contextual information that relies upon action sequences of the user and historical users, and user intent. The use of a multitask deep machine learning model allows for the model to generate action recommendations that are personalized, contextual, and coordinate across various different aspects of the online network, rather than being limited to only a single aspect. Likewise, the multi-task deep machine learning model can also be tailored to optimized different use-case specific objectives while at the same time being easy to scale and maintain.

Machine learning for predicting next best action

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.

Deep segment personalization

The present invention provides a re-partitioning-based sampling system and method which provides for generating a synopsis from large database tables such that an aggregation query performed on the synopsis provides an approximate answer to the aggregation query which is in prescribed error bounds relative to a query on the full database. The system includes a partition function generator, a synopsis vector calculator, and a synopsis constructor. The synopsis constructed by the system is sufficiently small to be held in memory to allow quick and resource efficient satisficing of aggregation queries.

System and method for creating an intelligent synopsis of a database using re-partitioning based sampling

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.

Load balancing using progressive sampling based on load balancing quality targets

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Palo Alto, CA, United States of America

Smriti R Ramakrishnan