Oracle International Corporation

Natalie You

Wenhua Li

Yizhou Jiang

Vivek Nama

Tuo Shi

Eran Cedar

Quan Yuan

Muhan Zou

Vivek S Shah

Michael James Strutton

Chad Estes

Kimberly Ann Wolfe

Dawei Huang

Tara U Roberts

John Benjamin Nolt

Steven Keith Woods

Donald Chun Wing Ho

A single tracking URL collects tracking information for multiple tracking services based on multiple tracking URL templates. A dynamic link generator generates a tracking URL configured to collect tracking information requested by each of a set of enabled tracking URL templates. The tracking information is transmitted to the tracking services associated with the enabled tracking URL templates. Additionally or alternatively, a tracking URL is configured to redirect a consumer to one of a candidate set of destination URLs. A tracking URL includes a base URL, which is resolved to an IP address of a server of a particular tracking service. Based on tracking data associated with the tracking URL and/or digital body language associated with the consumer, the particular tracking service selects one of a candidate set of destination URLs associated with the tracking URL. A destination page corresponding to the selected destination URL is presented to the consumer.

Generating tracking URLs and redirecting from tracking URLs

Techniques for using machine learning to configure social media campaigns are disclosed. A social relationship management (SRM) service performs supervised machine learning to generate a learned model, at least by: generating feature vectors based on training data including campaign configuration data and one or more campaign success metrics; and performing pattern recognition on the feature vectors to determine one or more preferred campaign configurations. The SRM service publishes messages to one or more social media platforms and receives user interaction data associated with users' interactions with the messages. The SRM service performs unsupervised machine learning to update the learned model based at least in part on the user interaction data. The SRM service receives a request to configure a social media campaign, applies data associated with the request to the learned model to determine a preferred campaign configuration, and configures the social media campaign based on the preferred campaign configuration.

Machine learning system for configuring social media campaigns

The present disclosure provides a detailed description of techniques used in systems, methods, and in computer program products for bi-directional social media broker services that connect multiple social media sites using a common management framework. The claimed embodiments address the problem of efficiently scaling and managing bidirectional interaction with multiple social media publishing channels. More specifically, the claimed embodiments are directed to approaches for a computing platform having multiple message broker modules that enable bi-directional communication of messages between a common resource manager and a plurality of social media sites. The message broker modules can receive messages in a unified message format from the common resource manager, queue the messages for asynchronous processing, translate the messages from the unified message format to a site-specific format, and deliver the translated messages to multiple social media sites.

Bi-directional multi-channel social media brokering

A social media enrichment framework is described herein. In one or more embodiments, the framework is configured to store a set of interface endpoints for accessing different sources of analytic metadata. The framework further stores a set of mappings between social media object types and interface endpoints. Responsive to receiving a social media object having a particular social media object type, the framework identifies, within the set of mappings, a mapping between the particular social media object type and at least one interface endpoint in the set of interface endpoints. The framework then obtains, from one or more sources using the at least one interface endpoint, a set of one or more metrics relevant to the social media object and stores the set of one or more metrics in association with the social media object.

Social media enrichment framework

Growing community of inventors

Mountain View, CA, United States of America

Natalie You