REAL-TIME CONTENT RECOMMENDATION SYSTEM

• US 20160210321A1
• Filed: 01/16/2015
• Published: 07/21/2016
• Est. Priority Date: 01/16/2015
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Gong, Xiaohong, Zhang, Wei, Asgharbeygi, Nima
• Granted Patent: US 9,858,308 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06F 16/24 Querying
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9536 Search customisation based on social or collaborative filtering
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: H04L 67/30 Profiles
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies

Abstract

System and methods of this disclosure are directed to recommending content in real-time or near real-time. The system comprises a number of pipelines updated a different frequencies that process temporally different sets of web property visit data. Within each pipeline, the system can employ different number of algorithms to process visit data to generate content recommendations. One algorithm is a content filter that filters from the visit data those determined to be unsuitable as recommendations. Another is a content analyzer that analyzes the content of each URL in the visit data by topic category and attribute. Another is an item-to-item collaborative filter that determines a correlation score for each URL based on those in the visit data in a single session Another is a device-to-item matrix factorization that determines an affinity score for each URL based on visit data, context information, and topic category.

Claims

1. A method of providing recommendations of real-time content, comprising:

selecting, by a first selection engine executing on a data processing system, a first set of content item identifiers from a first database of a content publisher at a first frequency;

updating, by an update engine executing on the data processing system, a first pipeline with the first set of content item identifiers;

selecting, by a second selection engine executing on the data processing system, a second set of content item identifiers from a second database of the content publisher at a second frequency, the second frequency different from the first frequency;

updating, by the update engine, a second pipeline with the second set of content item identifiers; and

producing, by an amalgamator engine executing on the data processing system, a combined set of content item identifiers comprising some of the first set of content item identifiers and some of the second set of content item identifiers.

2. The method of claim 1, wherein selecting the second set of content item identifiers comprises:

selecting, by the second selection engine executing on the data processing system, the second set of content item identifiers from the second database of the content publisher, the second database different from the first database, at the second frequency, the second frequency different from the first frequency.

3. The method of claim 1, wherein selecting the first set of content item identifiers further comprises:

choosing, by a profile filter module, a set of premium profiles from a set of profiles; and

identifying, by a content collector module, the first set of content item identifiers based on the set of premium profiles.

4. The method of claim 1, wherein selecting at least one of the sets of content item identifiers further comprises:

filtering, by a respective content filter module, from the respective set of content item identifiers a set of content item identifiers determined to be unsuitable.

5. The method of claim 4, comprising:

filtering, by a respective content filter module, from the respective set of content item identifiers a set of content item identifiers determined to be a homepage, a hub page, or a private page.

6. The method of claim 4, wherein selecting at least one of the sets of content item identifiers further comprises:

retrieving, via a respective content analysis engine, a respective set of topic categories and a respective set of topic category attributes; and

mapping, by a respective content analysis engine, each content item identifier to each other content item identifier in the respective set of content item identifiers based on the respective set of topic categories and the respective set of topic category attributes to generate a categorized respective set of content item identifiers.

7. The method of claim 6, further comprising:

correlating, by an item-to-item collaborative filter, each content item identifier to each other content item identifier in the categorized respective set of content item identifiers based on a dataset of clicks through each content item identifier to generate a correlation score and a correlated respective set of content item identifiers; and

ranking, by the item-to-item collaborative filter, the correlated respective set of content item identifiers based on the correlation score per content item identifier.

8. The method of claim 1, further comprising:

selecting, by a third selection engine, a third set of content item identifiers from a third database of the content publisher at a third frequency, the third frequency different from the first frequency and the second frequency; and

updating, by the update engine, a third pipeline, with the third set of content item identifiers.

9. The method of claim 8, producing the combined set of content item identifiers further comprises:

producing, by the amalgamator engine, the combined set of content item identifiers comprising some of the first set of content item identifiers, some of the second set of content item identifiers, and some of the third set of content item identifiers.

10. The method of claim 9, further comprising:

factorizing, by a device-to-item matrix factorization module, each content item identifier in the third set of content item identifiers to a set of devices based on a dataset of clicks, a set of device attributes, and a set of context information to generate an affinity score and a factorized third set of content item identifiers; and

ranking, by the device-to-item matrix factorization module, each content item identifier in the factorized third set of content item identifiers based on the affinity score for each content item identifier.

11. The method of claim 10, further comprising:

multiplying, by a combiner module, the correlation score and the affinity score per content item identifier in the third set of content item identifiers; and

generating, by the combiner module, a final score for each content item identifier in the third set of content item identifiers.

12. The method of claim 11, further comprising:

ranking, by a ranker module, each content item identifier in the third set of content item identifiers based on a linear regression of the correlation score and the affinity score parameterized by a click feedback set.

13. The method of claim 8, further comprising:

selecting, by the update engine, a fourth set of content item identifiers from a fourth database of the content publisher at a fourth frequency, the fourth frequency different from the first frequency, the second frequency, and third frequency;

updating, by the update engine, a fourth pipeline at a fourth frequency with the fourth set of content item identifiers; and

producing, by the amalgamator engine, the combined set of content item identifiers comprising some of the first set of content item identifiers, some of the second set of content item identifiers, some of the third set of content item identifiers, and some of the fourth set of content item identifiers.

14. A system for providing recommendations of real-time content comprising:

a first selection engine executing on a data processing system selecting a first set of content item identifiers from a first database of a content publisher at a first frequency;

a second selection engine executing on the data processing system selecting a second set of content item identifiers from a second database of the content publisher at a second frequency, the second frequency different from the first frequency;

an update engine executing on the data processing system updating a first pipeline with the first set of content item identifiers and a second pipeline with the second set of content item identifiers; and

an amalgamator engine executing on the data processing system producing a combined set of content item identifiers comprising some of the first set of content item identifiers and some of the second set of content item identifiers.

15. The system of claim 14, wherein the first selection engine further comprises:

a profile filter module choosing a set of premium profiles from a set of profiles; and

a content collector module identifying the first set of content item identifiers based on the set of premium profiles.

16. The system of claim 15, comprising:

the respective content filter module filtering from the respective set of content item identifiers a set of content item identifiers determined to be a homepage, a hub page, or a private page.

17. The system of claim 14, wherein at least one of the selection engines further comprises:

a respective content analysis engine retrieving a respective set of topic categories and a respective set of topic category attributes; and

the respective content analysis engine mapping each content item identifier to each other content item identifier in the respective set of content item identifiers based on the respective set of topic categories and the respective set of topic category attributes to generate a categorized respective set of content item identifiers.

18. The system of claim 17, further comprising:

an item-to-item collaborative filter correlating each content item identifier to each other content item identifier in the categorized respective set of content item identifiers based on a dataset of clicks through each content item identifier to generate a correlation score and a correlated respective set of content item identifiers; and

the item-to-item collaborative filter further ranking the correlated respective set of content item identifiers based on the correlation score per content item identifier.

19. The system of claim 14, further comprising:

a third selection engine executing on the data processing system selecting a third set of content item identifiers from a third database of the content publisher at a second frequency, the third frequency different from the first frequency;

the update engine further updating a third pipeline with the third set of content item identifiers and a third pipeline with the third set of content item identifiers;

the amalgamator engine executing on the data processing system further producing a combined set comprising some of the first set of content item identifiers, some of the second set of content item identifiers, and some of the third set of content item identifiers.

20. The system of claim 19, further comprising:

a fourth selection engine executing on the data processing system selection a fourth set of content item identifiers from a fourth database of the content publisher at a fourth frequency, the fourth frequency different from the first frequency, the second frequency, and third frequency;

the update engine updating a fourth pipeline with the fourth set of content item identifiers and a fourth pipeline with the fourth set of content item identifiers; and

the amalgamator engine executing on the data processing system further producing a combined set of content item identifiers comprising some of the first set of content item identifiers, some of the second set of content item identifiers, some of the third set of content item identifiers, and some of the fourth set of content item identifiers.