System And Method For Synchronous Recommendations of Social Interaction Spaces to Individuals

• US 20090106040A1
• Filed: 10/22/2008
• Published: 04/23/2009
• Est. Priority Date: 10/23/2007
• Status: Abandoned Application

• Current Assignee: New Jersey Institute of Technology
• Original Assignee: New Jersey Institute of Technology
• Inventors: Jones, Quentin
• Application Number:
  US12/256,084
• Publication Number:
  US 20090106040A1
• Time in Patent Office: Days
• US Class Current: 705/319
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A system and method for recommending a social interaction space to an individual are disclosed. Real-time information about users and a social interaction environment are gathered. A real-time, general model of the social interaction environment, including social interaction space states, trajectories, and rhythms, is created, as well as real-time profiles of the users. Affinities are calculated between modeled environment and user profiles, and social interaction spaces of the modeled environment are recommended to the user based upon the calculated affinities.

Claims

1. A method for recommending a social interaction space to an individual, comprising the steps of:

creating a model of a social interaction environment based upon real-time information gathered about the social interaction environment, wherein the social interaction environment includes a plurality of social interaction spaces;

creating a profile of an individual based upon user information gathered relating to an individual'"'"'s behavior and preferences;

graphing the model and the profile;

calculating an affinity matrix utilizing the graph of the model and the graph of the profile, wherein the affinity matrix is indicative of similarities therebetween; and

recommending at least one of the social interaction spaces for the individual utilizing the affinity matrix.

2. The method of claim 1, wherein the step of calculating the affinity includes extrapolating future preferences of the individual based on historical data.

3. The method of claim 1, further comprising monitoring interactions of the individual within the social interaction environment and updating the model based upon the monitored interactions.

4. The method of claim 3, further comprising recommending at least one of the social interaction spaces to the individual based upon the monitored interactions.

5. The method of claim 1, further comprising optimizing future recommendations for the individual.

6. The method of claim 5, wherein the future recommendations are optimized by at least one of:

(i) processing historical data (ii) monitoring the individual'"'"'s reaction to prior recommendations, (iii) processing prior recommendations using a collaborative filtering process, (iv) processing prior recommendations using social network data, and (v) correlating predicted states and observed states of the social interaction spaces.

7. The method of claim 6, wherein the state of each social interaction space is predicted using one or more techniques selected from the group comprising:

(i) analyzing short-term trajectory data corresponding to the social interaction space, (ii) analyzing long-term trajectory data corresponding to the social interaction space, (iii) applying a time-series analysis to the social interaction space, and (iv) analyzing scheduling data corresponding to the social interaction space.

8. The method of claim 7, wherein regression modeling of the historical data for the social interaction space is used to weight each of the one or more techniques.

9. The method of claim 1, further comprising recommending at least one of the social interaction spaces for the individual based upon group preferences.

10. The method of claim 1, further comprising recommending at least one of the social interaction spaces for the individual by measuring affinities between a social network structure of the individual and the model of the social interaction environment.

11. The method of claim 1, further comprising presenting recommendations to the individual through a conversational interface.

12. The method of claim 1, further comprising presenting recommendations to the individual through a social visualization technique.

13. A system for recommending a social interaction space to an individual, comprising:

a first software module for gathering real-time information about a social interaction environment, wherein the social interaction environment includes a plurality of social interaction spaces;

a second software module for gathering user information relating to an individual'"'"'s behavior and preferences;

means for creating a model of the social interaction environment based upon the real-time information;

means for creating a profile of the individual based upon the user information;

means for calculating an affinity between the model of the social interaction environment and the profile of the individual; and

means for recommending at least one of the social interaction spaces for the individual utilizing the affinity.

14. The system of claim 13, wherein the means for calculating the affinity includes means for extrapolating future preferences of the individual based on historical data.

15. The system of claim 13, further comprising means for monitoring interactions of the individual within the social interaction environment and means for updating the model based upon the monitored interactions.

16. The system of claim 15, further comprising means for recommending at least one of the social interaction spaces to the individual based upon the monitored interactions.

17. The system of claim 13, further comprising means for optimizing future recommendations.

18. The system of claim 17, wherein the means for optimizing future recommendations includes means for at least one of (i) processing historical data gathered by the first and second software modules (ii) monitoring the individual'"'"'s reaction to prior recommendations, (iii) processing prior recommendations using a collaborative filtering process, (iv) processing prior recommendations using social network data, and (v) correlating predicted states and observed states of the social interaction spaces.

19. The system of claim 18, further including means for predicting the state of each social interaction space using one or more techniques selected from the group comprising:

(i) analyzing short-term trajectory data corresponding to the social interaction space, (ii) analyzing long-term trajectory data corresponding to the social interaction space, (iii) applying a time-series analysis to the social interaction space, and (iv) analyzing scheduling data corresponding to the social interaction space.

20. The system of claim 19, wherein regression modeling of the historical data is used to weight each of the one or more techniques.