Particle thompson sampling for online matrix factorization recommendation

Patent metadata

Field	Value
Publication number	US-10332015-B2
Application number	US-201514885799-A
Country	US
Kind code	B2
Filing date	Oct 16, 2015
Priority date	Oct 16, 2015
Publication date	Jun 25, 2019
Grant date	Jun 25, 2019

Abstract

Particle Thompson Sampling for online matrix factorization recommendation is described. In one or more implementations, a recommendation system provides a recommendation of an item to a user using Thompson Sampling. The recommendation system then receives a rating of the item from the user. Unlike conventional solutions which only update the user latent features, the recommendation system updates both user latent features and item latent features in a matrix factorization model based on the rating of the item. The updating is performed in real time which enables the recommendation system to quickly adapt to the user ratings to provide new recommendations. In one or more implementations, to update the user latent features and the item latent features in the matrix factorization model, the recommendation system utilizes a Rao-Blackwellized particle filter for online matrix factorization.

First claim

What is claimed is:

1. A recommendation system comprising:
one or more server computing devices comprising one or more modules implemented at least partially in hardware and configured to perform operations comprising:
generating a recommendation of an item of a plurality of items for a user by applying a bandit algorithm to a matrix factorization model to select the item from the plurality of items;
communicating the recommendation of the item to a client device of the user over a network;
receiving, over the network, a rating of the item from the client device of the user;
updating, in real-time, both a user latent feature of the user and an item latent feature of the item of the matrix factorization model based on the rating of the item;
generating an additional recommendation of an additional item of the plurality of items for the user by applying the bandit algorithm to the updated matrix factorization model to select the additional item from the plurality of items; and
communicating the additional recommendation of the additional item to the client device of the user over the network.

2. The recommendation system of claim 1 , wherein the bandit algorithm comprises a Thompson Sampling algorithm.

3. The recommendation system of claim 1 , wherein the updating comprises updating, in real-time, both the user latent feature of the user and the item latent feature of the item of the matrix factorization model in an online setting.

4. The recommendation system of claim 3 , wherein the updating is performed by a Rao-Blackwellized particle filter.

5. The recommendation system of claim 1 , wherein the matrix factorization model associates ratings of items with user latent features and item latent features.

6. The recommendation system of claim 1 , wherein the applying the bandit algorithm to the matrix factorization model causes the recommendation system to automatically combine finding relevant items with exploring new or less-relevant items.

7. The recommendation system of claim 1 , wherein the recommended item comprises one or more of a product, a song, a movie, or an advertisement.

8. The recommendation system of claim 1 , wherein the rating comprises an explicit rating.

9. The recommendation system of claim 1 , wherein the rating comprises an implicit rating.

10. A computer-implemented method comprising:
receiving, over a network, a rating of a recommended item from a client device of a user in an online setting; and
applying a Rao-Blackwellized particle filter to update, in real-time, both user latent features and item latent features of the matrix factorization model based on the rating to enable an additional recommendation of an additional item to the user based on the updated matrix factorization model.

11. The computer-implemented method of claim 10 , wherein the recommended item is recommended using a matrix factorization model.

12. The computer-implemented method of claim 10 , wherein the rating comprises an explicit rating or an implicit rating.

13. The computer-implemented method of claim 10 , wherein the recommended item comprises one or more of a product, a song, a movie, or an advertisement.

14. A computer-implemented method comprising:
generating, at a computing device, a recommendation of an item of a plurality of items for a user by applying Thomson Sampling to a matrix factorization model;
communicating the recommendation of the item to a client device of the user over a network;
receiving, over the network, a rating of the item from the client device of the user;
updating, in real-time, the matrix factorization model based on the rating of the item; and
generating an additional recommendation of an additional item for the user by applying Thompson Sampling to the updated matrix factorization model.

15. The computer-implemented method of claim 14 , wherein the updating comprises updating the matrix factorization model using a Rao-Blackwellized particle filter.

16. The computer-implemented method of claim 15 , wherein the Rao-Blackwellized particle filter updates the matrix factorization model by updating both user latent features and item latent features in the matrix factorization model based on the rating of the item.

17. The computer-implemented method of claim 14 , wherein the applying Thompson Sampling to the matrix factorization model causes the recommendation system to automatically combine finding relevant items with exploring new or less-relevant items.

18. The computer-implemented method of claim 14 , wherein the updating is performed in an online setting.

19. The computer-implemented method of claim 14 , wherein the rating comprises an explicit rating or an implicit rating.

20. The computer-implemented method of claim 10 , further comprising communicating the additional recommendation of the additional item to the client device of the user over the network.

Assignees

• Adobe Inc

Inventors

• Kawale Jaya B
• Kveton Branislav
• Bui Hung H

Classifications

• G06F17/16 Primary

  Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} · CPC title

• G06N7/01 Primary

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06Q10/067

  Enterprise or organisation modelling · CPC title

• G06Q30/0282

  Rating or review of business operators or products · CPC title

• G06N7/005 Primary

  Physics · mapped topic