Salesforce Com, Inc.

Salesforce, Inc.

Tejas Sanghavi

Marco Casalaina

A prediction model in a database system may be configured to predict, for a given object instance, a respective probability of acceptance for each of a plurality of recommendations. A determination may be made as to whether the prediction model is associated with sufficient training data to produce predictions at a designated accuracy rate. When it is determined that the prediction model is not associated with sufficient training data, for each of a first set of object instances a respective first message may be sent that includes a respective first one of the recommendations determined based on a static ranking rule applying one or more criteria to one or more object fields associated with the respective object instance. The prediction model may be updated to include additional training data based on a plurality of responses corresponding to a respective first one of the recommendations.

Dynamic ranking of recommendation pairings

A graphical user interface (GUI) may be provided by a computing system that implements a database system for presentation at a client device. The GUI may display a designated one or more criteria for selecting one of a plurality of recommendations for a target object instance associated with a designated object definition. A predictive model for determining a propensity score for selected ones of the plurality of recommendations in association with the target object instance may be configured. The propensity score may be a function of one or more data field values associated with the target object instance and may be configured based on user input received via the graphical user interface. The predictive model may be stored on a storage medium for retrieval when selecting recommendations in response to requests received to access instances of the designated object definition.

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San Francisco, CA, United States of America

Tejas Sanghavi