Content recommendation system using a neural network language model

Patent metadata

Field	Value
Publication number	US-9535897-B2
Application number	US-201314136111-A
Country	US
Kind code	B2
Filing date	Dec 20, 2013
Priority date	Dec 20, 2013
Publication date	Jan 3, 2017
Grant date	Jan 3, 2017

Abstract

The present disclosure relates to applying techniques similar to those used in neural network language modeling systems to a content recommendation system. For example, by associating consumed media content to words of a language model, the system may provide content predictions based on an ordering. Thus, the systems and techniques described herein may produce enhanced prediction results for recommending content (e.g. word) in a given sequence of consumed content. In addition, the system may account for additional user actions by representing particular actions as punctuation in the language model.

First claim

The invention claimed is:

1. A computer-implemented method of providing recommendations, comprising:
obtaining a user history for a user, the user history identifying a plurality of items, the plurality of items comprising items representing one or more media items presented to the user and items representing one or more actions performed by the user;
generating a sequence of tokens that includes a respective token associated with each of the one or more media items presented to the user and a respective token associated with each of the one or more actions performed by the user;
providing each token in the sequence of tokens as an input to a recurrent neural network that is configured to process each of the tokens and, after processing a last token in the sequence of tokens, predict a next token subsequent to the last token in the sequence of tokens; and
providing a recommendation to the user based on an item associated with the predicted next token.

2. The computer-implemented method of claim 1 , wherein the one or more actions performed by the user include one of selecting an advertisement, performing a search, visiting a webpage, navigating a webpage, rating a media item, sharing a media item, and interrupting a played media item.

3. The computer-implemented method of claim 1 , wherein the one or more actions performed by the user include ending a session.

4. The computer-implemented method of claim 1 , wherein the one or more media items presented to the user include videos, music, documents, or applications.

5. The computer-implemented method of claim 1 , wherein the recurrent neural network is configured to associate each of a plurality of candidate tokens with a respective probability.

6. The computer-implemented method of claim 5 , wherein the recommendation to the user is provided as a list identifying items associated with the plurality of candidate tokens for the next token.

7. The computer-implemented method of claim 6 , wherein the items associated with the plurality of candidate tokens for the next token are ordered in the list based on probabilities associated with the plurality of candidate tokens.

8. A system for providing recommendations, comprising:
a processor, the processor configured to:
obtain a user history for a user, the user history identifying a plurality of items, the plurality of items comprising items representing one or more media items presented to the user and items representing one or more actions performed by the user;
generate a sequence of tokens that includes a respective token associated with each of one or more media items presented to the user and a respective token associated with each of the one or more actions performed by the user;
provide each token in the sequence of tokens as an input to a recurrent neural network that is configured to process each of the tokens and, after processing a last token in the sequence of tokens, predict a next token subsequent to the last token in the sequence of tokens; and
provide a recommendation to the user based on an item associated with the predicted next token.

9. The system of claim 8 , wherein the one or more actions performed by the user include one of selecting an advertisement, performing a search, visiting a webpage, navigating a webpage, rating a media item, sharing a media item, and interrupting a played media item.

10. The system of claim 8 , wherein the one or more actions performed by the user include ending a session.

11. The system of claim 8 , wherein the one or more media items presented to the user include videos, music, documents, or applications.

12. The system of claim 8 , wherein the recurrent neural network is configured to associate each of a plurality of candidate tokens with a respective probability.

13. The system of claim 12 , wherein the recommendation to the user is provided as a list identifying items associated with the plurality of candidate tokens for the next token.

14. The system of claim 13 , wherein the items associated with the plurality of candidate tokens for the next token are ordered in the list based on probabilities associated with the plurality of candidate tokens.

Assignees

• Google Inc

Inventors

• Anderson Glen
• Schuster Michael

Classifications

• G06F16/954

  Navigation, e.g. using categorised browsing · CPC title

• H04N21/4668

  for recommending content, e.g. movies · CPC title

• G06F16/951

  Indexing; Web crawling techniques · CPC title

• G06Q30/0631

  Recommending goods or services · CPC title

• G06F40/274

  Converting codes to words; Guess-ahead of partial word inputs · CPC title