Building a social graph with sharing activity between users of the open web and inferring interest of a node based on edges between first-degree and second-degree nodes

• US 9,317,610 B2
• Filed: 01/14/2014
• Issued: 04/19/2016
• Est. Priority Date: 06/16/2011
• Status: Active Grant

• Current Assignee: R1 Demand, LLC
• Original Assignee: Radiumone (Tremor International Ltd.)
• Inventors: Vaynblat, Dimitri, Chahal, Gurbaksh
• Primary Examiner(s): Barry, Lance L
• Application Number:
  US14/155,267
• Publication Number:
  US 20140129660A1
• Time in Patent Office: 826 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/25 Integrating or interfacing systems involving database management systems
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: G06Q 30/0244 Optimization
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0271 Personalized advertisement
• CPC Class Codes: G06Q 30/0273 Determination of fees for advertising
• CPC Class Codes: G06Q 30/0275 Auctions
• CPC Class Codes: G06Q 30/0277 : Online advertisement
• CPC Class Codes: G06Q 30/0641 : Shopping interfaces
• CPC Class Codes: G06Q 50/01 : Social networking
• CPC Class Codes: H04L 12/1813 : for computer conferences, e.g. chat rooms instant messaging H04L51/04; protocols for multimedia communication H04L65/1101; arrangements for multi-party communication H04L65/403; telephonic conference arrangements H04M3/56; television conference systems H04N7/15
• CPC Class Codes: H04L 41/12 : Discovery or management of network topologies
• CPC Class Codes: H04L 43/045 : for graphical visualisation of monitoring data
• CPC Class Codes: H04L 43/06 : Generation of reports
• CPC Class Codes: H04L 51/04 : Real-time or near real-time messaging, e.g. instant messaging [IM]
• CPC Class Codes: H04L 51/046 : Interoperability with other network applications or services
• CPC Class Codes: H04L 51/10 : Multimedia information
• CPC Class Codes: H04L 51/52 : for supporting social networking services
• CPC Class Codes: H04L 51/56 : Unified messaging, e.g. interactions between e-mail, instant messaging or converged IP messaging [CPM]
• CPC Class Codes: H04L 67/01 : Protocols
• CPC Class Codes: H04L 67/02 : based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/51 : Discovery or management thereof, e.g. service location protocol [SLP] or web services
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 67/565 : Conversion or adaptation of application format or content adding application control or application functional data H04L67/561
• CPC Class Codes: H04W 4/12 : Messaging; Mailboxes; Announcements

Abstract

A social graph is built which includes interactions, sharing activity, and connections between the users of the open Web and can be used to improve ad targeting and content personalization. Sharing activity between two users will affect ads or content that both users will be presented while surfing the Web. This sharing activity includes sending of links, sending of videos, sending of files, cutting and pasting of content, sending text messages, and sending of e-mails. Interest of a node can be inferred based on edges between first-degree and second-degree nodes.

Claims

1. A method comprising:

collecting activity data from a plurality of Web sources using a plurality of collection devices;

based the activity data, forming a social graph having a first node and a plurality of first-degree nodes connected to the first node, wherein each first-degree node is connected to a plurality of second-degree nodes;

determining edges between first-degree and second-degree nodes comprise a first category type;

determining no edges exist between the first node and the first-degree nodes of the first category type; and

based on the edges of the first category type between first-degree and second-degree nodes, using at least one processor, making an inference that the first node has an interest associated with the first category type.

2. The method of claim 1 wherein the collection devices comprise URL shortening.

3. The method of claim 1 wherein the collection devices comprise instant messaging.

4. The method of claim 1 wherein the collection devices comprise a sharing widget.

5. The method of claim 1 comprising:

identifying a plurality of users and sharing activity between the users in the activity data.

6. The method of claim 5 wherein each edge between nodes in the social graph represents a different sharing category.

7. The method of claim 5 wherein the identifying a plurality of users and the sharing activity between the users in the activity data comprises:

extracting a user identifier from a cookie in the activity data; and

identifying the first node in the social graph based on the user identifier.

8. The method of claim 7 comprising:

updating a value of an edge coupled to the first node.

9. The method of claim 1 wherein the activity data does not contain any personally identifiable information.

10. The method of claim 1 wherein the forming a social graph having a first node and a plurality of first-degree nodes connected to the first node is based on the collected activity data, which does not contain any personally identifiable information.

11. The method of claim 1 comprising:

based on the inference that the first node has an interest associated with the first category type, selecting an advertisement associated with the first category type for online delivery to a user corresponding to the first node in the social graph.

12. The method of claim 1 comprising:

based on the inference that the first node has an interest associated with the first category type, making a bid to an ad exchange for an advertisement associated with the first category type for to a user corresponding to the first node in the social graph.

13. A method comprising:

collecting activity data from a plurality of Web sources using a plurality of collection devices. wherein the activity data does not contain any personally identifiable information;

based the activity data, forming a social graph having a first node and a plurality of first-degree nodes connected to the first node, wherein each first-degree node is connected to a plurality of second-degree nodes;

determining edges between first-degree and second-degree nodes comprise a first category type;

determining no edges exist between the first node and the first-degree nodes of the first category type;

based on the edges of the first category type between first-degree and second-degree nodes, using at least one processor, making an inference that the first node has an interest associated with the first category type; and

based on the inference that the first node has an interest associated with the first category type, selecting an advertisement associated with the first category type for delivery to a user corresponding to the first node in the social graph.

14. The method of claim 13 comprising:

identifying a plurality of users and sharing activity between the users in the activity data.

15. The method of claim 14 wherein the identifying a plurality of users and the sharing activity between the users in the activity data comprises:

extracting a user identifier from a cookie in the activity data; and

identifying the first node in the social graph based on the user identifier.

16. The method of claim 15 comprising:

updating a value of an edge coupled to the first node.

17. A method comprising:

collecting activity data from a plurality of Web sources using a plurality of collection devices, wherein the activity data does not contain any personally identifiable information;

based the activity data, forming a social graph having a first node and a plurality of first-degree nodes connected to the first node, wherein each first-degree node is connected to a plurality of second-degree nodes, and the social graph has been formed without the use of any personally identifiable information;

determining edges between first-degree and second-degree nodes comprise a first category type;

determining no edges exist between the first node and the first-degree nodes of the first category type;

based on the edges of the first category type between first-degree and second-degree nodes, using at least one processor, making an inference that the first node has an interest associated with the first category type; and

based on the inference that the first node has an interest associated with the first category type, selecting an advertisement associated with the first category type for delivery to a user corresponding to the first node in the social graph.

18. The method of claim 17 wherein the collection devices comprise URL shortening.

19. The method of claim 17 wherein the collection devices comprise instant messaging.

20. The method of claim 17 wherein the collection devices comprise a sharing widget.