Capital One Services, LLC

Dwipam Katariya

Tania Cruz Morales

Julian Duque

Muhammad Uddin

Kimberly Stockley

In some implementations, a system may receive interaction data associated with interactions between a user and subsets of a plurality of interaction parties. The system may store the interaction data and the as historical interaction data associated with historical interactions of the user. The system may provide the historical interaction data as input to a machine learning model, which may be trained using supervised learning and the historical interactions of the user or historical interactions of one or more other users with one or more of the plurality of interaction parties. The system may receive an output, based on applying the machine learning model to the historical interaction data, that may indicate one or more recommended interaction parties based at least in part on one or more factors, wherein the one or more recommended parties may be local entities local to a geographic location associated with the user.

Machine learning model for recommending interaction parties

Disclosed embodiments can include a system for generating experiences. A system receives user data related to interactions by the user. The system identifies a set of trends for the user based on the interactions. The system receives a locational indicator, the locational indicator providing a first destination. The system searches locational data associated with the first destination to identify one or more experiences for the user. The system compares the set of trends with the locational data. The system calculates a first experience for the user. The system outputs, for display on a user device, the first experience.

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McLean, VA, United States of America

Dwipam Katariya