Personalization enhanced recommendation models

Patent metadata

Field	Value
Publication number	US-11250347-B2
Application number	US-201816134726-A
Country	US
Kind code	B2
Filing date	Sep 18, 2018
Priority date	Jun 27, 2018
Publication date	Feb 15, 2022
Grant date	Feb 15, 2022

Abstract

Methods, systems, apparatuses, and computer program products are provided for a two-phase technique for generating content recommendations. In a first phase, a baseline recommender is configured to generate a baseline content recommendation using one or more content recommendation models, such as a Smart Adaptive Recommendations (SAR) model, Factorization Machine (FM) or Matrix Factorization (MF) models, collaborative filtering models, and/or any other machine-learning models or techniques. In a second phase, a personalized recommender implements a vector combiner configured to combine profile vectors, content vectors, and the baseline content recommendations to generate combined user vectors. A model generator may train a machine-learning model using the combined user vectors and training data comprising actual interaction behavior of the users, which may be then applied to identify a content recommendation for a particular user.

First claim

What is claimed is:

1. A system for generating a machine-learning model for providing a content recommendation, the system comprising:
at least one processor; and
memory that stores program code configured to be executed by the at least one processor, the program code comprising:
a baseline recommender configured to generate baseline content recommendations using historical user-content interactions by a plurality of users;
a personalized recommender comprising:
a profile vector generator configured to generate profile vectors corresponding to the users based on user profile information of the users;
a content vector generator configured to generate content vectors corresponding to the users based on content interaction data of the users;
a vector combiner configured to generate user vectors for the users, each user vector generated for a user corresponding to a first time period and including a baseline content recommendation of the baseline content recommendations, a profile vector of the profile vectors, and a content vector of the content vectors corresponding to the user; and
a model generator configured to:
retrieve interaction training data corresponding to tracked interactions, by the users, with content of a plurality of content types corresponding to a second time period shorter than the first time period; and
generate a recommendation model using a supervised machine-learning algorithm that receives the interaction training data and user vectors as inputs, the recommendation model configured to identify content for recommendation to users.

2. The system of claim 1 , wherein the content interaction data indicates, for each of the users, an amount of interaction with content of the content types on a video game console.

3. The system of claim 1 , wherein the vector combiner is configured to generate a second user vector corresponding to a third time period for a particular user; and
wherein the system further comprises:
an enhanced content recommendation engine configured to apply the second user vector to the recommendation model to identify content for recommendation to the particular user.

4. The system of claim 1 , wherein the interaction training data comprises, for each of the users, a click-through rate indicating tracked interactions of the user with content for each of the content types.

5. The system of claim 1 , wherein the baseline content recommendations comprise a ranking of baseline content recommendations for each of the users.

6. The system of claim 1 , wherein the baseline recommender comprises a plurality of recommendation models, each recommendation model configured to generate baseline content recommendations using historical user-content interactions by the users.

7. The system of claim 1 , wherein the first time period does not overlap with the second time period.

8. A method in a computing device for generating a machine-learning model for providing a content recommendation, the method comprising:
generating baseline content recommendations using historical user-content interactions by a plurality of users;
generating profile vectors corresponding to the users based on user profile information of the users;
generating content vectors corresponding to the users based on content interaction data of the users;
generating combined user vectors for the users, each user vector generated for a user corresponding to a first time period and including a baseline content recommendation of the baseline content recommendations, a profile vector of the profile vectors, and a content vector of the content vectors corresponding to the user;
retrieving interaction training data corresponding to tracked interactions, by the users, with content of a plurality of content types corresponding to a second time period shorter than the first time period; and
generating a recommendation model using a supervised machine-learning algorithm that receives the interaction training data and user vectors as inputs, the recommendation model configured to identify content for recommendation to users.

9. The method of claim 8 , wherein the content interaction data indicates, for each of the users, an amount of interaction with content of the content types on a video game console.

10. The method of claim 8 , further comprising:
generating a second user vector corresponding to a third time period for a particular user; and
applying the second user vector to the recommendation model to identify content for recommendation to the particular user.

11. The method of claim 8 , wherein the interaction training data comprises, for each of the users, a click-through rate indicating tracked interactions of the user with content for each of the content types.

12. The method of claim 8 , wherein the baseline content recommendations comprise a ranking of baseline content recommendations for each of the users.

13. The method of claim 8 , wherein said generating the baseline content recommendation comprises generating the baseline content recommendations using historical user-content interactions by the users for each of a plurality of recommendation models.

14. The method of claim 8 , wherein the first time period does not overlap with the second time period.

15. A computer-readable medium having computer program code recorded thereon that when executed by at least one processor causes the at least one processor to perform a method comprising:
generating baseline content recommendations using historical user-content interactions by a plurality of users;
generating profile vectors corresponding to the users based on user profile information of the users;
generating content vectors corresponding to the users based on content interaction data of the users;
generating combined user vectors for the users, each user vector generated for a user corresponding to a first time period and including a baseline content recommendation of the baseline content recommendations, a profile vector of the profile vectors, and a content vector of the content vectors corresponding to the user;
retrieving interaction training data corresponding to tracked interactions, by the users, with content of a plurality of content types corresponding to a second time period shorter than the first time period; and
generating a recommendation model using a supervised machine-learning algorithm that receives the interaction training data and user vectors as inputs, the recommendation model configured to identify content for recommendation to users.

16. The computer-readable medium of claim 15 , wherein the content interaction data indicates, for each of the users, an amount of interaction with content of the content types on a video game console.

17. The computer-readable medium of claim 15 , further comprising:
generating a second user vector corresponding to a third time period for a particular user; and
applying the second user vector to the recommendation model to identify content for recommendation to the particular user.

18. The computer-readable medium of claim 15 , wherein the baseline content recommendations comprise a ranking of baseline content recommendations for each of the users.

19. The computer-readable medium of claim 15 , wherein said generating the baseline content recommendation comprises outputting a ranked set of baseline content recommendations for each of the users.

20. The computer-readable medium of claim 15 , wherein the first time period does not overlap with the second time period.

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Cai Yaxiong
• Qi Xiaoguang
• Yang Kiyoung
• Su Shih-Chieh
• Azzam Saliha
• Nanduri Jayaram N M

Classifications

• G06F9/30036

  Instructions to perform operations on packed data, e.g. vector, tile or matrix operations · CPC title

• G06N5/02

  Knowledge representation; Symbolic representation · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06F16/9535 Primary

  Search customisation based on user profiles and personalisation · CPC title