Adaptive social computing methods

Abstract

Methods of applying adaptive social computing systems are disclosed. The social computing systems include capabilities to generate adaptive recommendations and representations of social networks derived, at least in part, from inferences of the preferences and interests of system users based on a plurality of usage behaviors, spanning a plurality of usage behavior categories. The behavioral categories include system navigation behaviors, content referencing behaviors, collaborative behaviors, and the monitoring of physical location and changes in location. Privacy control functions and compensatory functions related to insincere usage behaviors can be applied. Adaptive recommendation delivery can take the form of visual-based or audio-based formats.

What is claimed is: 
     
       1. An adaptive recommendation method comprising:
 interacting with a content aspect comprising information; 
 interacting with a computer-implemented structural aspect comprising the content aspect and associated relationships; 
 contributing behaviors to a usage aspect, the usage aspect comprising captured usage behaviors, wherein the usage behaviors are associated with one or more users corresponding to a plurality of usage behavior categories; and 
 applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, the adaptive recommendation being based, at least in part, on an automatic inference of a preference of a user from a plurality of the captured usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories. 
 
     
     
       2. The method of  claim 1 , wherein the information is selected from a group consisting of text, graphics, audio, video, interactive forms of content, applets, tutorials, advertising content, courseware, demonstrations, representations of people, modules, executable code, and computer programs. 
     
     
       3. The method of  claim 1 , wherein the structural aspect further comprises:
 one or more objects, each object comprising the information; and 
 one or more relationships, wherein each relationship is associated with each pair of the one or more objects. 
 
     
     
       4. The method of  claim 1 , the usage aspect further comprising one or more usage behaviors, wherein each usage behavior is associated with either a user, one or more user communities, or a user and one or more user communities simultaneously, wherein the user comprises a single-member subset of the one or more users and a community of the one or more user communities comprises a multiple-member subset of the one or more users. 
     
     
       5. The method of  claim 1 , further comprising a privacy control, the privacy control enabling a user of the one or more users to restrict usage behaviors associated with the user from being deemed non-private behaviors. 
     
     
       6. The method of  claim 1 , wherein applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, the adaptive recommendation being based, at least in part, on an automatic inference of a preference of a user from a plurality of the captured usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories comprises:
 applying usage behavior categories, wherein the usage behavior categories are selected from a group consisting of navigation and access patterns, collaborative patterns, direct feedback patterns, subscription patterns, self-profiling patterns, reference patterns, and physical location patterns. 
 
     
     
       7. The method of  claim 1 , applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, the adaptive recommendation being based, at least in part, on an automatic inference of a preference of a user from a plurality of the captured usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories comprises:
 inferring a user interest derived from, at least in part, usage behaviors. 
 
     
     
       8. The method of  claim 1 , wherein applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, the adaptive recommendation being based, at least in part, on an automatic inference of a preference of a user from a plurality of the captured usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories:
 applying a compensatory algorithm associated with the detection of apparent insincere system usage behaviors or other inferred “gaming” behaviors by the one or more users. 
 
     
     
       9. The method of  claim 1 , wherein applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, the adaptive recommendation being based, at least in part, on an automatic inference of a preference of a user from a plurality of the captured usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories comprises:
 applying an algorithm that performs pattern matching of information embodied in the structural aspect and content aspect to produce content interpretation patterns, and associates the content interpretation patterns with usage patterns. 
 
     
     
       10. The method of  claim 1 , wherein applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, the adaptive recommendation being based, at least in part, on an automatic inference of a preference of a user from a plurality of the captured usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories comprises:
 receiving a recommendation in a delivery mode, wherein the recommendation delivery mode is selected from a group consisting of visual, audio, and a combination of visual and audio. 
 
     
     
       11. The method of  claim 1 , wherein applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, the adaptive recommendation being based, at least in part, on an automatic inference of a preference of a user from a plurality of the captured usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories comprises:
 modifying a structural element, wherein the structural element is selected from the group consisting of a function to generate a new relationship, a function to modify an existing relationship, a function to delete an existing relationship, a function to add an object, a function to modify an object, and a function to delete an object. 
 
     
     
       12. A mobile adaptive recommendation method comprising:
 contributing behaviors to a computer-implemented usage aspect, the usage aspect comprising captured usage behaviors, wherein the usage behaviors are associated with one or more users; and 
 receiving on a mobile device an automatically generated user tunable adaptive recommendation based, at least in part, on automatically determining the location of a first user of the one or more users and on at least one other usage behavior of the captured usage behaviors associated with the one or more users corresponding to at least one other usage behavior category. 
 
     
     
       13. The method of  claim 12 , further comprising:
 determining the change in location of a user as a function of time. 
 
     
     
       14. The method of  claim 12 , further comprising:
 receiving the automatically generated adaptive recommendation, wherein the recommendation is based, at least in part, on the first user's proximity to a second user. 
 
     
     
       15. A method comprising:
 operating a content aspect comprising information;   operating a computer-implemented structural aspect comprising the content aspect and relationships associated with the content aspect;   operating a usage aspect comprising captured usage behaviors, wherein the captured usage behaviors are associated with one or more users and correspond to a plurality of usage behavior categories; and   applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, wherein the user tunable adaptive recommendation is based, at least in part, on an automatic inference of a preference of the one or more users from a plurality of the captured usage behaviors associated with the one or more users.   
     
     
       16. The method of claim 15, wherein the information comprises one or more text, graphics, audio, video, interactive forms of content, applets, tutorials, advertising content, courseware, demonstrations, representations of people, modules, executable code, or computer programs. 
     
     
       17. The method of claim 15, wherein:
 the computer-implemented structural aspect comprises one or more objects comprising the information; and   the relationships are associated with the one or more objects.   
     
     
       18. The method of claim 15, wherein the captured usage behaviors are associated with a single-member subset of the one or more users or a multiple-member subset of the one or more users. 
     
     
       19. The method of claim 15, further comprising restricting designation of the captured usage behaviors as non-private behaviors. 
     
     
       20. The method of claim 15, wherein the usage behavior categories comprise one or more navigation and access patterns, collaborative patterns, direct feedback patterns, subscription patterns, self-profiling patterns, reference patterns, or physical location patterns. 
     
     
       21. The method of claim 15, wherein said applying a user tunable adaptive recommendation further comprises inferring an interest of the one or more users from the captured usage behaviors. 
     
     
       22. The method of claim 15, wherein said applying a user tunable adaptive recommendation further comprises identifying apparent insincere system usage behaviors or inferred gaming behaviors by the one or more users. 
     
     
       23. The method of claim 15, wherein said applying a user tunable adaptive recommendation further comprises:
 pattern matching the information to produce content interpretation patterns, and   associating the content interpretation patterns with usage patterns.   
     
     
       24. The method of claim 15, wherein said applying a user tunable adaptive recommendation comprises presenting the user tunable adaptive recommendation as visual data, audio data, or visual data and audio data. 
     
     
       25. The method of claim 15, wherein said applying a user tunable adaptive recommendation comprises modifying at least some of the relationships, deleting at least some of the relationships, generating new relationships, adding an object, modifying the object, or deleting the object. 
     
     
       26. A method comprising:
 providing inputs to a computer-implemented usage aspect, wherein the computer-implemented usage aspect comprises captured usage behaviors and the captured usage behaviors are associated with one or more users; and   receiving on a mobile device an automatically generated user tunable adaptive recommendation based, at least in part, on automatically determining a location of the one or more users and on at least one of the captured usage behaviors associated with the one or more users, wherein the captured usage behaviors correspond to at least one usage behavior category.   
     
     
       27. The method of claim 26, further comprising determining a change in the location of the one or more users as a function of time. 
     
     
       28. The method of claim 26, wherein the automatically generated user tunable adaptive recommendation is based, at least in part, on a proximity of a first one of the one or more users to a second one of the one or more users. 
     
     
       29. An apparatus, comprising:
 a content aspect comprising information;   a structural aspect comprising the content aspect and relationships within the content aspect;   a usage aspect comprising captured usage behaviors, wherein the captured usage behaviors are associated with users and correspond to usage behavior categories; and   logic circuitry configured to provide user tunable adaptive recommendations for navigating the structural aspect, wherein the user tunable adaptive recommendations are based on inferences of preferences of the users derived from the captured usage behaviors.   
     
     
       30. The apparatus of claim 29, wherein the user tunable adaptive recommendations are tunable by non-users. 
     
     
       31. The apparatus of claim 29, wherein the information comprises text, graphics, audio, video, interactive forms of content, applets, tutorials, advertising content, courseware, demonstrations, representations of people, modules, executable code, or computer programs. 
     
     
       32. The apparatus of claim 29, wherein:
 the structural aspect comprises objects associated with the information; and   the relationships are between the objects.   
     
     
       33. The apparatus of claim 29, wherein the captured usage behaviors are associated with a single-member subset of the users or a multiple-member subset of the users. 
     
     
       34. The apparatus of claim 29, wherein the logic circuitry is further configured to restrict access to the captured usage behaviors. 
     
     
       35. The apparatus of claim 29, wherein the usage behavior categories comprise one or more navigation and access patterns, collaborative patterns, direct feedback patterns, subscription patterns, self-profiling patterns, reference patterns, or physical location patterns. 
     
     
       36. The apparatus of claim 29, wherein the logic circuitry is further configured to infer interests of the users based on the captured usage behaviors. 
     
     
       37. The apparatus of claim 29, wherein the logic circuitry is further configured to detect apparent insincere system usage behaviors by the users. 
     
     
       38. The apparatus of claim 29, wherein the logic circuitry is further configured to:
 identify content interpretation patterns from the information, and   associate the content interpretation patterns with usage patterns for the users.