Generating digital event recommendation sequences utilizing a dynamic user preference interface

Patent metadata

Field	Value
Publication number	US-2021325193-A1
Application number	US-202117364480-A
Country	US
Kind code	A1
Filing date	Jun 30, 2021
Priority date	Jul 27, 2018
Publication date	Oct 21, 2021
Grant date	—

Abstract

The present disclosure relates to generating and modifying recommended event sequences utilizing a dynamic user preference interface. For example, in one or more embodiments, the system generates a recommended event sequence using a recommendation model trained based on a plurality of historical event sequences. The system then provides, for display via a client device, the recommendation, a plurality of interactive elements for entry of user preferences, and a visual representation of historical event sequences. Upon detecting input of user preferences, the system can modify a reward function of the recommendation model and provide a modified recommended event sequence together with the plurality of interactive elements. In one or more embodiments, as a user enters user preferences, the system additionally modifies the visual representation to display subsets of the plurality of historical event sequences corresponding to the preferences.

First claim

What is claimed is:

1 . A computer-implemented method for generating and providing digital sequence recommendations based on interactive user interface preference elements, comprising:
generating a popular event sequence based on a plurality of historical event sequences; generating, utilizing a recommendation machine learning model, a recommended event sequence; receiving, from a client device, one or more user preferences corresponding to one or more events; generating, utilizing the recommendation machine learning model, a modified recommended event sequence based on the one or more user preferences; and providing, for display within a graphical user interface on the client device, the recommended event sequence, the modified recommended event sequence, and the popular event sequence.

2 . The computer-implemented method of claim 1 , wherein generating, utilizing the recommendation machine learning model, the modified recommended event sequence based on the one or more user preferences comprises:
modifying a reward function of the recommendation machine learning model using the one or more user preferences; and generating the modified recommended event sequence utilizing the recommendation machine learning model with the modified reward function.

3 . The computer-implemented method of claim 2 , further comprising:
determining a first expected value score corresponding to the modified recommended event sequence using the modified reward function; and providing, for display within the graphical user interface on the client device, the first expected value score in conjunction with the modified recommended event sequence.

4 . The computer-implemented method of claim 3 , further comprising:
determining, using the modified reward function, a second expected value score corresponding to the popular event sequence; determining, using the modified reward function, a third expected value score corresponding to the recommended event sequence; and providing, for display within the graphical user interface on the client device, the second expected value score in conjunction with the popular event sequence and the third expected value score in conjunction with the recommended event sequence.

5 . The computer-implemented method of claim 2 , wherein modifying the reward function of the recommendation machine learning model based on the one or more user preferences comprises providing a preference weight to one or more parameters of the reward function that correspond to the one or more user preferences.

6 . The computer-implemented method of claim 1 , further comprising:
providing, for display within the graphical user interface on the client device, a first set of visual indicators corresponding to events of the recommended event sequence; providing, for display within the graphical user interface on the client device, a second set of visual indicators corresponding to events of the modified recommended event sequence; and providing, for display within the graphical user interface on the client device, a third set of visual indicators corresponding to events of the popular event sequence.

7 . The computer-implemented method of claim 6 ,
further comprising determining that a first event from the events of the modified recommended event sequence is more popular than a second event from the events of the modified recommended event sequence, wherein providing the third set of visual indicators corresponding to the events of the modified recommended event sequence comprises providing a first visual indicator corresponding to the first event having a larger size than a second visual indicator corresponding to the second event based on determining that the first event is more popular than the second event.

8 . The computer-implemented method of claim 1 , wherein:
receiving the one or more user preferences comprises receiving at least one of a preferred point of interest category or a preferred point of interest; and generating the modified recommended event sequence based on the one or more user preferences comprises generating the modified recommended event sequence to include at least one of a point of interest associated with the preferred point of interest category or to include the preferred point of interest.

9 . The computer-implemented method of claim 1 , further comprising:
receiving, from an additional client device, one or more additional user preferences corresponding to the one or more events; generating, utilizing the recommendation machine learning model, an additional modified recommended event sequence based on the one or more additional user preferences; and providing, for display within a graphical user interface on the additional client device, the recommended event sequence, the additional modified recommended event sequence, and the popular event sequence.

10 . A non-transitory computer-readable medium storing instructions thereon that, when executed by at least one processor, cause a computing device to:
generate a popular event sequence based on a plurality of historical event sequences; generate, utilizing a recommendation machine learning model, a recommended event sequence; provide, for display within a graphical user interface of a client device, the popular event sequence, the recommended event sequence, and a plurality of interactive elements for entry of user preferences corresponding to one or more events; determine one or more user preferences based on at least one user interaction with the plurality of interactive elements via the graphical user interface of the client device; and in response to determining the one or more user preferences:
generate, utilizing the recommendation machine learning model, a modified recommended event sequence based on the one or more user preferences; and
modify the graphical user interface of the client device to display the recommended event sequence, the modified recommended event sequence, and the popular event sequence.

11 . The non-transitory computer-readable medium of claim 10 , further comprising instructions that, when executed by the at least one processor, cause the computing device to:
provide, for display within the graphical user interface of the client device, a visual representation of historical event sequences from the plurality of historical event sequences that correspond to the recommended event sequence; and modify, in response to determining the one or more user preferences, the graphical user interface of the client device to display a visual representation of a subset of historical event sequences from the plurality of historical event sequences that correspond to the one or more user preferences.

12 . The non-transitory computer-readable medium of claim 10 , further comprising instructions that, when executed by the at least one processor, cause the computing device to:
provide for display within the graphical user interface of the client device, a map display that displays a plurality of points of interest associated with the recommended event sequence or the popular event sequence and a recommended trajectory for visiting the plurality of points of interest; and modify, in response to determining the one or more user preferences, the map display to display points of interest associated with the modified recommended event sequence and a recommended trajectory for visiting the points of interest.

13 . The non-transitory computer-readable medium of claim 10 , further comprising instructions that, when executed by the at least one processor, cause the computing device to:
provide, for display within the graphical us

Assignees

• Adobe Inc

Inventors

• Du Fan
• Lee Sana Malik
• Theocharous Georgios
• Koh Eunyee

Classifications

• G01C21/3484

  Personalized, e.g. from learned user behaviour or user-defined profiles · CPC title

• H04W4/024

  Guidance services · CPC title

• G01C21/3476

  using point of interest [POI] information, e.g. a route passing visible POIs · CPC title

• G01C21/343 Primary

  Calculating itineraries (travelling salesman problem G06Q10/04; optimisation of routes G06Q10/047) · CPC title

• G06Q10/047 Primary

  Optimisation of routes or paths, e.g. travelling salesman problem · CPC title