Optimizing personalized recommendations with longitudinal data and a future objective

• US 20150363502A1
• Filed: 06/16/2014
• Published: 12/17/2015
• Est. Priority Date: 06/16/2014
• Status: Abandoned Application

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Uchida, Makoto, Chen, Aiyou, Koehler, James Robert, Remy, Nicolas
• Application Number:
  US14/305,607
• Publication Number:
  US 20150363502A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

Systems and techniques are provided for optimizing personalized recommendations with longitudinal data and a future objective. An identifier may be received for content items. A user content item history including a list identifying a previously acquired content item may be received. Content item metadata may be received including a correlation between the previously acquired content item and a content item for which an identifier was received, and a correlation between a content item for which an identifier was received and fulfillment of a future objective. A joint probability may be determined for each content item based on the user content item history and the content item metadata, including the probability that the content item will be acquired by the user after being recommended to the user and that a future objective will be fulfilled after the content item is acquired by the user.

Claims

1. A computer-implemented method performed by a data processing apparatus, the method comprising:

receiving an identifier for each of at least two content items;

receiving a user content item history for a user, wherein the user content item history comprises a list identifying at least one previously acquired content item;

receiving content item metadata comprising at least one correlation between the at least one previously acquired content item and at least one of the content items for which an identifier was received, and at least one correlation between at least one of the content items for which an identifier was received and a fulfillment of a future objective;

determining a joint probability for each of the at least two content items based on the user content item history and the content item metadata, wherein the joint probability for one of the content items comprises the probability that the one of the content items will be acquired by the user after being recommended to the user and that a future objective will be fulfilled after the one of the content items is acquired by the user; and

sending the content item identifier for the content item with the highest joint probability from the at least two content items to be viewed by the user.

2. The computer-implemented method of claim 1, wherein fulfilling the future objective comprises at least one of:

the user rating the content item with the highest joint probability, the user reviewing the content item with the highest joint probability, the user recommending the content item with the highest joint probability, and the user purchasing another content item different from the content item with the highest joint probability within a specified timeframe.

3. The computer-implemented method of claim 1, wherein the content item is an application, a music track, a music album, a TV show episode, a TV show season, or a movie.

4. The computer-implemented method of claim 1, wherein the joint probability is determined according to:

P(Y=1|x₀,x₁, . . . ,x_t)×

T_xtxt+1(1)+(1−

P(Y=1|x₀,x₁, . . . ,x_t))T_xtxt+1(0),

wherein x₀ is a null content item, x₁, . . . , x_t is the user content item history, Y represents whether or not the future objective will be met, x_t+1 is the content item for which the joint probability is being determined, T_xtxt+1(0) is a transition probability when the future objective will not be met, T_xtxt+1(1) is a transition probability when the future objective will be met, and x₀=0 represents when there are no content items in the user content item history.

5. The computer-implemented method of claim 4, wherein T_xtx+1(1) is determined according to P(X_t+1=k|X_t=j,Y=1) and T_xtxt+1(0) is determined according to P(X_t+1=k|X_t=j, Y=0).

6. The computer-implemented method of claim 5, wherein P(X_t+1=k|X_t=j, Y=1) and P(X_t+1=k|X_t=j, Y=0) are determined based on the user content item history and content item metadata.

7. The computer-implemented method of claim 4, wherein P (Y=1|x₀, x₁, . . . , x_t) is determined according to

8. The computer-implemented method of claim 1, wherein sending the identifier for the content item with the highest joint probability comprises sending the identifier to a content ecosystem application running on a client device.

9. The computer-implemented method of claim 1, wherein sending the identifier for the content item with the highest joint probability comprises sending the identifier in an email to an email account associated with the user.

10. The computer-implemented method of claim 1, wherein the user content item history comprises longitudinal data for the user.

11. A computer-implemented system for optimizing personalized recommendations with longitudinal data and a future objective:

a storage comprising a content database, the content database comprising content items, user content item histories, and content item metadata; and

a content recommender adapted to receive a user account identifier that identifies a user, a user content item history associated with the user from the user content item histories, the content item metadata, and content item identifiers for at least two of the content items from the content database, evaluate a joint probability for each of the at least two content items based on the user content item history and the content item metadata, and send an identifier for the content item with the highest joint probability from the at least two content items to the user.

12. The computer-implemented system of claim 11, wherein the joint probability for one of the content items comprises a probability that the user will acquire the one of the content items and a future objective will be fulfilled after the user acquires the one of the content items.

13. The computer-implemented system of claim 11, wherein the user content item history comprises a list identifying at least one previously acquired content item.

14. The computer-implemented system of claim 11, wherein the content item metadata comprises at least one correlation between two content items from the content database, and at least one correlation between at least one of the content items from the content database and fulfillment of a future objective

15. The computer-implemented system of claim 12, wherein fulfilling the future objective comprises at least one of:

the user rating the content item with the highest joint probability, the user reviewing the content item with the highest joint probability, the user recommending the content item with the highest joint probability, and the user purchasing another content item different from the content item with the highest joint probability within a specified timeframe.

16. The computer-implemented system of claim 11, wherein the content item is an application, a music track, a music album, a TV show episode, a TV show season, or a movie.

17. The computer-implemented system of claim 11, wherein the joint probability is determined according to:

P(Y=1|x₀,x₁, . . . ,x_t)×

T_xtxt+1(1)+(1−

P(Y=1|x₀,x₁, . . . ,x_t))T_xtxt+1(0),

wherein x₀ is a null content item, x₁, . . . , x_t is the user content item history, Y represents whether or not the future objective will be met, x_t+1 is the content item for which the joint probability is being determined, T_xtxt+1(0) is a transition probability when the future objective will not be met, T_xtxt+1(1) is a transition probability when the future objective will be met, T_xtxt+1 is a transition probability, and x₀=0 represents when there are no content items in the user content item history.

18. The computer-implemented system of claim 17, wherein T_xtxt+1(1) is determined according to P(X_t+1=k|X_t=j, Y=1) and T_xtxt+1(0) is determined according to P(X_t+1=k|X_t=j, Y=0).

19. The computer-implemented system of claim 18, wherein P(X_t+1=k|X_t=j, Y=1) and P(X_t+1=k|X_t=j, Y=0) are determined based on the user content item history and content item metadata.

20. The computer-implemented system of claim 17, wherein P (Y=1|x₀, x₁, . . . ,x_t) is determined according to

21. The computer-implemented system of claim 11, wherein the content recommender is further adapted to send the identifier for the content item with the highest joint probability to a content ecosystem application running on a client device.

22. The computer-implemented method of claim 11, wherein the content recommender is further adapted to send the identifier for the content item with the highest joint probability in an email to an email account associated with the user.

23. A system comprising:

one or more computers and one or more storage devices storing instructions which are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising;

receiving an identifier for each of at least two content items;

receiving a user content item history for a user, wherein the user content item history comprises a list identifying at least one previously acquired content item;

receiving content item metadata comprising at least one correlation between the at least one previously acquired content item and at least one of the content items for which an identifier was received, and at least one correlation between at least one of the content items for which an identifier was received and fulfillment of a future objective;

determining a joint probability for each of the at least two content items based on the user content item history and the content item metadata, wherein the joint probability for one of the content items comprises the probability that the one of the content items will be acquired by the user after being recommended to the user and that a future objective will be fulfilled after the one of the content items is acquired by the user; and

sending the content item identifier for the content item with the highest joint probability from the at least two content items to be viewed by the user.