Abstract
The need for reconciling data originating from disparate sources exists in multiple domains such as digital maps for products such as restaurant reservations, appointments, food ordering and pickup; and artist attribution for products such as event ticketing etc. While domain-specific reconciliation infrastructure exists, such infrastructure generally does not work outside the domain that it is designed for. This disclosure describes techniques to reconcile different types of entities in a scalable and reliable manner by leveraging signals from automation (software running on a server) and humans (e.g., crowdsourced, internal engineering or operations teams, external partners, etc.) so that multiple domains can leverage these techniques through a single platform. In addition, the techniques also provide a prioritization framework for human-generated signals thereby facilitating better quality controls.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Ranganathan, Sandeep and Seckar, Nicholas, "Reconciling Third-party Data by Leveraging Automated and Human Signals", Technical Disclosure Commons, (August 22, 2024)
https://www.tdcommons.org/dpubs_series/7302