Social Data Ranking and Processing

• US 20110213785A1
• Filed: 12/28/2010
• Published: 09/01/2011
• Est. Priority Date: 02/26/2010
• Status: Active Grant

• Current Assignee: Telefonaktiebolaget LM Ericsson
• Original Assignee: Telefonaktiebolaget LM Ericsson
• Inventors: KRISTIANSSON, Johan, HALLBERG, Josef, SYNNES, Kåre, HÅKANSSON, Stefan, RANA, Juwel
• Granted Patent: US 8,943,053 B2
• Time in Patent Office: Days
• US Class Current: 707/748
• CPC Class Codes: G06F 16/435 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 51/52 for supporting social networking 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 67/54 Presence management, e.g. monitoring or registration for receipt of user log-on information, or the connection status of the users
• CPC Class Codes: H04L 67/564 Enhancement of application control based on intercepted application data

Abstract

Social networking services, such as Facebook, Twitter, MySpace and Orkut, are changing our communication habits by introducing new tools that simplify and increase on-line interaction. The exemplary embodiments present methods for measuring the social strength among contacts on the basis of users'"'"' on-line interactions, which may be significantly used in real-time collaborative task management (e.g., inviting contacts for collaboration, selecting suitable tools for communication, prioritizing information flow). Moreover, exemplary embodiments provide a framework for developing new mobile social networking applications (social search client, smart dialer or contact applications) by extending the use of social ranking of contacts. Exemplary embodiments aggregate contact information from different communication sources and then harness the interaction patterns together with social strength between contacts, for instance by using communication history (interaction logs) and process mining techniques, to build an aggregated social graph that describes a user'"'"'s communication habit.

Claims

1. A method for providing a ranked list of a plurality of contacts based upon aggregated communication history data and contextual data, said method comprising:

receiving aggregated communication history data associated with each of said plurality of contacts, wherein said aggregated communication history data for at least some of said plurality of contacts includes data from different communication services;

receiving a request for said ranked list, said request including contextual data;

calculating a rank for each contact based on the contextual data and that contact'"'"'s aggregated communication history data; and

transmitting said ranked list toward a requestor.

2. A method for providing a ranked list of a plurality of contacts, comprising:

receiving communication history data associated with each of said plurality of contacts, wherein said communication history data for at least one of said plurality of contacts includes data from more than one communication service;

receiving a request for said ranked list;

calculating a rank for each of said plurality of contacts based on the request and the communication history data; and

transmitting said ranked list toward a requestor.

3. The method of claim 2, wherein the request includes contextual data, wherein the contextual data is used when calculating the rank for each contact.

4. The method of claim 3, wherein calendar data is included in said communication history data.

5. The method of claim 3, wherein the rank for each contact based on the contextual data and the communication history data is ranked relative to a particular user.

6. The method of claim 2, wherein said request for said ranked list is a contacts recommendation request.

7. The method of claim 2, wherein said request for said ranked list is a presence information request.

8. The method of claim 2, wherein said request for said ranked list is a content or messages prioritizing request.

9. The method of claim 2, wherein said request for said ranked list is a filtering decisions request.

10. The method of claim 2, wherein said request for said ranked list is received from a web application.

11. The method of claim 2, further comprising transmitting said rank for each contact toward the requestor.

12. The method of claim 3, wherein the rank for each contact based on the contextual data and the communication history data is calculated using a social strength metric.

13. The method of claim 12, wherein the social strength metric comprises one or more members of the group consisting of frequency of communication, form of communication, contact lists, and context.

14. The method of claim 2, wherein said data from said more than one communication service for said at least one of said plurality of contacts comprises data from different social networks, and wherein data from each social network comprises data from multiple communication tools.

15. The method of claim 3, wherein the calculating of the rank for each contact based on the contextual data and the communication history data comprises:

calculating a first social strength measure on the basis of a first communication service of the more than one communication service for said at least one of said plurality of contacts; and

calculating a second social strength measure on the basis of all of the more than one communication services for said at least one of said plurality of contacts.

16. The method of claim 15, wherein the calculating of the second social strength measure comprises considering a frequency of usage of each of the more than one communication services for said at least one of said plurality of contact.

17. The method of claim 2, wherein the calculating of the rank is calculated according to the algorithm:

18. The method of claim 3, wherein the calculating of the rank for each contact based on the contextual data and the communication history comprises measuring a Euclidean distance between each contact and a particular user associated with said request.

19. The method of claim 18, wherein the measuring of the Euclidean distance is calculated according to the algorithm

20. A computing system, comprising:

a communication history aggregator configured to aggregate communication history data associated with a plurality of contacts, wherein said aggregated communication history data for at least one of said plurality of contacts includes data from a plurality of different communication services;

an aggregated social graph service configured to receive a request for a ranked list; and

a social ranker configured to calculate a rank of each contact after receipt of the request for the ranked list by the aggregated social graph service, said rank being based on the request and the aggregated communication history data,

wherein said ranked list is transmitted toward a requestor.

21. The computing system of claim 20, wherein said request includes contextual data, and said social ranker is further configured to calculate said rank of each contact based on said contextual data.

22. The computing system of claim 21, wherein the rank for each contact based on the contextual data and the aggregated communication history data is ranked relative to a particular user.

23. The computing system of claim 20, wherein said request for said ranked list is a contacts recommendation request.

24. The computing system of claim 20, wherein said rank for each contact is transmitted toward the requestor.

25. The computing system of claim 20, wherein the social ranker is configured to calculate the rank for each contact using a social strength metric.

26. The computing system of claim 25, wherein the social strength metric comprises one or more members of the group consisting of frequency of communication, form of communication, contact lists, and context.

27. The computing system of claim 20, wherein said data from different communication services for said at least one of said plurality of contacts comprises data from different social networks, and wherein data from each social network comprises data from multiple communication tools.

28. The computing system of claim 20, wherein the social ranker is configured to:

calculate a first social strength measure on the basis of a first communication service of the different communication services for said at least one of said plurality of contacts; and

calculate a second social strength measure on the basis of all of the different communication sources for said at least one of said plurality of contacts.

29. The computing system of claim 28, wherein the social ranker is configured to calculate the second social strength measure considering a frequency of usage of each of the different communication services for said at least one of said plurality of contacts.

30. The computing system of claim 28, wherein the social ranker is configured to calculate the rank according to the algorithm

31. The computing system of claim 20, wherein the social ranker is configured to calculate the rank for each contact by measuring a Euclidean distance between each contact and a particular user associated with said request.

32. The computing system of claim 31, wherein the measuring of the Euclidean distance is calculated according to the algorithm

33. A data structure stored in a computer memory or on a computer readable storage, the data structure comprising:

a first field comprising a user identifier;

a second field comprising a first contact identifier of a first contact of the user, the first contact being from a first communication service;

a third field comprising a second contact identifier of a second contact of the user, the second contact being from a second communication service;

a fourth field comprising a ranking of a strength of a connection between the first contact of the user and the user; and

a fifth field comprising a ranking of a strength of a connection between the second contact of the user and the user.