- Number
- 20190340659
- Published
- 2019-11-07
- Filed
- 2018-05-03
- Assignee
- Disney Enterprises, Inc.
- Inventors
- CHAPMAN; Steven et al.
- CPC
- G06N3/0464; G06Q30/0282; G06N20/00; G06N3/09; G06F18/2178; G06F18/40; G06F16/337; G06N5/01; G06N3/045
- Verdict
- Set aside machine learning enabled evaluation systems, business ratings
- Source
- Google Patents · FreePatentsOnline
Abstract
Systems and methods for providing machine-learning enabled user-specific evaluations are disclosed. Implementations include obtaining a first set of evaluation data from a user interface, obtaining a first set of target-descriptive data including target-specific characteristics objectively describing the evaluation targets, and training, with a machine-learning algorithm, a user-specific evaluation profile indicating evaluation patterns relative to the first set of evaluation data and the first set of target-specific characteristics. Implementations include applying the user-specific evaluation profile to a second set of target-descriptive data to predict a user-specific evaluation.
Background
BACKGROUND
Product and service review tools, such as YELP, ROTTEN TOMATOES, AMAZON, TRIP ADVISOR, NETFLIX, and other similar review systems, provide users with aggregated review information. For example, the review system may provide an average number of “stars,” points, happy faces, tomatoes, or other relative ranking indicator for all reviews submitted. The same evaluation scores and descriptions may be provided to users interested in the particular good or service being reviewed, regardless of the users' individualized preferences and tastes. For example, a user who is particular about certain types of fried chicken may perceive and review a restaurant that serves chicken differently than a user who does not like chicken, or is ambiguous to chicken. In another example involving relative price rating systems, one user might think $5 for a meal is expensive, while another user might think $5 for a meal is cheap. While some ranking systems do attempt to learn a user's overall preference and recommend products or services (such as NETFLIX), those ranking systems are generally binary or numeric (e.g., thumbs up or thumbs down, or rank on a scale of 1 to 5).BRIEF SUMMARY OF EMBODIMENTS
According to various embodiments of the disclosed technology, systems and methods for enhancing evaluations and reviews with machine learning are described. In particular, the disclosed technology uses a machine learning algorithm to train a user-specific evaluation profile based on th
Claims
1. A computer implemented method of providing evaluations, the method comprising: obtaining a first set of evaluation data from a graphical user interface, the evaluation data indicating qualitative descriptive feedback from a user regarding multiple initial evaluation targets; obtaining a first set of target-descriptive data comprising, for each initial evaluation target, one or more target-specific characteristics objectively describing the evaluation target; and training, with a user evaluation profiling logical circuit, a user-specific evaluation profile indicating evaluation patterns relative to the first set of evaluation data and the first set of target-specific characteristics.
2. The computer-implemented method of claim 1, further comprising obtaining a review target from a review category and obtaining review target-specific characteristics objectively describing the review target; and determining, with the user evaluation prediction logical circuit, a predicted user review of the review target by applying the user-specific evaluation profile to the review target-specific characteristics.
3. The computer-implemented method of claim 2, further comprising obtaining, from the user interface, a user-created review of the review target and comparing the user-created review to the predicted user review to determine a review discrepancy.
4. The computer-implemented method of claim 3, further comprising modifying the user-specific evaluation profile by applying, with the user evaluation profiling logical circuit, the review discrepancy to the user-specific evaluation profile.
5. The computer-implemented method of claim 2, further comprising determining a probability that a user will prefer the review target as compared with other possible review targets selected from the review category.
6. The computer-implemented method of claim 5, further comprising filtering a list of review targets from the review category based on the probability that the user will prefer review targets in the list.
7. The computer-implemented method of claim 2, further comprising generating a user review of the review target based on the a predicted user review and displaying the user review on a graphical user interface.
8. The computer-implemented method of claim 1, wherein the initial evaluation targets comprise a movie, a restaurant, a travel service, a book, a consumer product, a video game, or a personal service.
9. The computer-implemented method of claim 2, wherein the review categories comprise movies, restaurants, travel services, books, consumer products, video games, or personal services.
10. The computer-implemented method of claim 1, wherein the user-specific evaluation profile comprises a convolutional neural network, a decision tree, or a linear regression model.
11. An evaluation system comprising: a user interface, a data store, and a user evaluation prediction logical circuit, the user evaluation prediction logical circuit comprising a processor and a non-transitory medium with computer executable instructions embedded thereon, the computer executable instructions configured to: obtain a first set of evaluation data from a user interface, the evaluation data indicating qualitative descriptive feedback from a user regarding multiple initial evaluation targets; obtain a first set of target-descriptive data comprising, for each initial evaluation target, one or more target-specific characteristics objectively describing the evaluation target; and train, with a user evaluation profiling logical circuit, a user-specific evaluation profile indicating evaluation patterns relative to the first set of evaluation data and the first set of target-specific characteristics.
12. The system of claim 11, wherein the computer executable instructions are further configured to cause the processor to: obtain a review target from a review category and obtaining review target-specific characteristics objectively describing the review target; and determine a predicted user review of the review target by applying the user-specific evaluation profile to the review target-specific characteristics.
13. The system of claim 12, wherein the computer executable instructions are further configured to cause the processor to obtain, from the user interface, a user-created review of the review target and compare the user-created review to the predicted user review to determine a review discrepancy.
14. The system of claim 13, wherein the computer executable instructions are further configured to cause the processor to modify the user-specific evaluation profile by applying the review discrepancy to the user-specific evaluation profile.
15. The system of claim 12, wherein the computer executable instructions are further configured to cause the processor to determine a probability that a user will prefer the review target as compared with other possible review targets selected from the review category.
16. The system of claim 15, wherein the computer executable instructions are further configured to cause the processor to filter a list of review targets from the review category based on the probability that the user will prefer review targets in the list.
17. The system of claim 12, wherein the computer executable instructions are further configured to cause the processor to generate a user review of the review target based on the a predicted user review and displaying the user review on a graphical user interface.
18. The system of claim 11, wherein the initial evaluation targets comprise a movie, a restaurant, a travel service, a book, a consumer product, a video game, or a personal service.
19. The system of claim 12, wherein the review categories comprise movies, restaurants, travel services, books, consumer products, video games, or personal services.
20. The system of claim 11, wherein the user-specific evaluation profile comprises a convolutional neural network, a decision tree, or a linear regression model.