Stochastic Multi-Modal Recommendation and Information Retrieval System

Number: 20230153664
Published: 2023-05-18
Filed: 2021-11-18
Assignee: Disney Enterprises, Inc.
Inventors: Salkey; Jayson
CPC: G06N3/045; G06N3/08; G06N7/01; G06N20/20; G06N5/01
Verdict: Set aside recommendation/information-retrieval system, business
Source: Google Patents · FreePatentsOnline

Abstract

A system includes a computing platform including processing hardware and a memory storing software code including a trained machine learning (ML) model. The processing hardware executes the software code to receive entity specific data over a network from a user device, identify mapping parameters of the entity specific data, and map, using the trained ML model and the mapping parameters, the entity specific data to a statistical distribution in a multi-dimensional representation space. The software code further compares, using the trained ML model, the mapped statistical distribution to each of one or more predetermined statistical distributions in the multi-dimensional representation space, predicts, to using the trained ML model and the comparison, a matching probability for each of the one or more predetermined statistical distributions relative to the mapped statistical distribution. generates a similarity set based on the prediction, and outputs the similarity set to the user device over the network.

Background

BACKGROUND

The volume of social media interactions and digital media content depicting sports, news, movies, television (TV) programming, print media, and music on digital platforms on the internet far exceeds the capacity of a user to discover and evaluate. Moreover, the sheer number of users of social media can make it difficult for any one user to identify other unfamiliar users with whom tastes and interests may be shared in common. Industrial-scale user and content modeling, as well as recommendation systems, are used to determine how users interact with items in order to model their interests and interaction behaviors.

Collaborative filtering has remained the dominant approach to making recommendations based on leveraging the modeled patterns between user interests and interactions with items. Deep learning models, optimized through gradient-based learning algorithms, have garnered interest at the industrial-scale for collaborative filtering tasks. However, a fundamental limitation of this approach is its inability to reconcile the popularity-bias inherent in the training data.

Claims

1. A system comprising: a computing platform including a processing hardware and a system memory; a software code including a trained machine learning (ML) model stored in the system memory; the processing hardware configured to execute the software code to: receive entity specific data over a network from a user device; identify a plurality of mapping parameters of the entity specific data; map, using the trained ML model and the plurality of mapping parameters, the entity specific data to a statistical distribution in a multi-dimensional representation space; perform a comparison, using the trained ML model, of the mapped statistical distribution to each of one or more predetermined statistical distributions in the multi-dimensional representation space; predict, using the trained ML model and the comparison, a matching probability for each of the one or more predetermined statistical distributions relative to the mapped statistical distribution; generate a similarity set based on the prediction; and output the similarity set to the user device over the network. || 11. A method for use by a system including a computing platform having a processing hardware and a system memory storing a software code including a trained machine learning (ML) model, the method comprising: receiving, by the software code executed by the processing hardware, entity specific data over a network from a user device; identifying, by the software code executed by the processing hardware, a plurality of mapping parameters of the entity specific data; mapping, by the software code executed by the processing hardware and using the trained ML model and the plurality of mapping parameters, the entity specific data to a statistical distribution in a multi-dimensional representation space; performing a comparison, by the software code executed by the processing hardware using the trained ML model, of the mapped statistical distribution to each of one or more predetermined statistical distributions in the multi-dimensional representation space; predicting, by the software code executed by the processing hardware and using the trained ML model and the comparison, a matching probability for each of the one or more predetermined statistical distributions relative to the mapped statistical distribution; generating, by the software code executed by the processing hardware, a similarity set based on the prediction; and outputting, by the software code executed by the processing hardware, the similarity set to the user device over the network.