Predictive analysis of target behaviors utilizing RNN-based user embeddings
US10558852B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 16, 2017 |
| Grant date | Feb 11, 2020 |
| Priority date | — |
| Expiry date | Dec 5, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/2411
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.