Salesforce, Inc.

Salesforce Com, Inc.

Leah McGuire

Shubha Umesh Nabar

Vitaly Gordon

Kin Fai Kan

Michael Weil

Mayukh Bhaowal

Xiaodan Sun

Christopher Rupley

Yan Yang

Sarah Aerni

Jean-Marc Soumet

Kit Pang Szeto

Karl Ryszard Skucha

Kevin Moore

Brian Brechbuhl

Matvey Tovbin

Vlad Patryshev

Leslie Fine

Marco Vivero

Joshua Sauter

Qiong Liu

Eric Wayman

John Grotland

Subha Nabar

Rick Munoz

Xiuchai (Meko) Xu

A set of data for training a machine learning system can be modified to improve its performance. An item of information can be transmitted. A message can be transmitted that includes an explanation of a determination, by the machine learning system, to transmit the item of information from among a plurality of items of information. A first set of data can have been used to train the machine learning system. A signal can be received that includes an indication of a usefulness of the message, to a user of a user device, in making a decision to perform an action based on a knowledge associated with the item of information. The first set of data can be modified, in response to a receipt of the signal, to produce a second set of data. The machine learning system can be caused to be trained using the second set of data.

Automatic generation of an explanation, based on data lineage and user feedback, of a prediction produced by a machine learning system

Methods and systems are provided to determine suitable hyperparameters for a machine learning model and/or feature engineering process. A suitable machine learning model and associated hyperparameters are determined by analyzing a dataset. Suitable hyperparameter values for compatible machine learning models having one or more hyperparameters in common and a compatible dataset schema are identified. Hyperparameters may be ranked according to each of their respective influences on a model performance metrics, and hyperparameter values identified as having greater influence may be more aggressively searched.

Identification and application of hyperparameters for machine learning

A system may generate a score for a predictive model based on receiving a streaming data flow of events associated with a predictive model for a tenant. The system may receive the streaming data flow and calculate one or more feature values in real time based on the reception. The system may store each of the calculated features to a multi-tenant database server. The system may calculate a score for the predictive model based on the storage and may transmit an indication of the score (e.g., a prediction) based on the calculation. The system may transmit the score to, for example, a computing device.

Feature generation and storage in a multi-tenant environment

A method for improving mass messaging in an electronic messaging system includes receiving recipient data describing a response of each of one or more recipients to receiving a prior message, generating predictor data based on the recipient data, where the predictor data indicates a plurality of predictors of recipient behavior in response to a message, identifying one or more top predictors of recipient behavior, the one or more top predictors being selected from among the plurality of predictors based on preferred recipient behaviors, generating, for each of the one or more recipients and from the recipient data, one or more predictive scores for each combination of top predictor and recipient, and assigning, based on one or more predictive scores of a specific recipient, the specific recipient to a specific persona, wherein the specific persona describes an expected behavior of the recipient.

Predictive scoring and messaging in messaging systems

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Redwood City, CA, United States of America

Leah McGuire