Auto-Learning Recommender Method and System

Abstract

An auto-learning recommender method and system delivers recommendations to users and analyzes the resulting usage behaviors by applying a computer-implemented neural network. Probabilities are automatically determined based upon the analysis that may correspond to inferred preferences. The probabilities inform the generation of additional recommendations that are delivered to users. The generation of the additional recommendations may be further informed by value of information calculations and/or analysis of intrinsic patterns within content.

What is claimed is: 
     
         1 . A method comprising:
 delivering automatically a first one or more recommendations to one or more users;   accessing automatically a first plurality of usage behaviors that are responsive to the first one or more recommendations;   applying automatically a computer-implemented neural network to analyze the first plurality of usage behaviors;   determining automatically one or more probabilities in accordance with the application of the computer-implemented neural network;   generating automatically a second one or more recommendations that are generated in accordance with the one or more probabilities; and   delivering the second one or more recommendations to the one or more users.   
     
     
         2 . The method of  claim 1 , further comprising:
 accessing the first plurality of usage behaviors, wherein the first plurality of usage behaviors are associated with a plurality of users.   
     
     
         3 . The method of  claim 1 , further comprising:
 determining automatically the one or more probabilities, wherein each of the one or more probabilities is associated with an inference of a preference.   
     
     
         4 . The method of  claim 1 , further comprising:
 generating automatically the second one or more recommendations, wherein the second one or more recommendations are further generated in accordance with a value of information.   
     
     
         5 . The method of  claim 4 , further comprising:
 generating further the second one or more recommendations in accordance with the value of information, wherein the value of information is determined by application of a monte carlo simulation.   
     
     
         6 . The method of  claim 1 , further comprising:
 generating automatically the second one or more recommendations, wherein the second one or more recommendations are further generated in accordance with an automatic analysis of intrinsic patterns within content.   
     
     
         7 . The method of  claim 6 , further comprising:
 generating automatically the second one or more recommendations that are further generated in accordance with the automatic analysis of the intrinsic patterns within the content, wherein the intrinsic patterns within the content are performed by a statistical analysis comprising a computer-implemented neural network.   
     
     
         8 . A system comprising one or more processor-based devices configured to:
 deliver automatically a first one or more recommendations to one or more users;   access automatically a first plurality of usage behaviors that are responsive to the first one or more recommendations;   apply automatically a computer-implemented neural network to analyze the first plurality of usage behaviors;   determine automatically one or more probabilities in accordance with the application of the computer-implemented neural network;   generate automatically a second one or more recommendations that are generated in accordance with the one or more probabilities; and   deliver the second one or more recommendations to the one or more users.   
     
     
         9 . The system of  claim 8 , further comprising the one or more processor-based devices configured to:
 access the first plurality of usage behaviors, wherein the first plurality of usage behaviors are associated with a plurality of users.   
     
     
         10 . The system of  claim 8 , further comprising the one or more processor-based devices configured to:
 determine automatically the one or more probabilities, wherein each of the one or more probabilities is associated with an inference of a preference.   
     
     
         11 . The system of  claim 8 , further comprising the one or more processor-based devices configured to:
 generate automatically the second one or more recommendations, wherein the second one or more recommendations are further generated in accordance with a value of information.   
     
     
         12 . The system of  claim 11 , further comprising the one or more processor-based devices configured to:
 generate further the second one or more recommendations in accordance with the value of information, wherein the value of information is determined by application of a decision tree.   
     
     
         13 . The system of  claim 8 , further comprising the one or more processor-based devices configured to:
 generate automatically the second one or more recommendations, wherein the second one or more recommendations are further generated in accordance with an automatic analysis of intrinsic patterns within content.   
     
     
         14 . The system of  claim 13 , further comprising the one or more processor-based devices configured to:
 generate automatically the second one or more recommendations that are further generated in accordance with the automatic analysis of the intrinsic patterns within the content, wherein the automatic analysis of the intrinsic patterns within the content are performed by a statistical analysis comprising a computer-implemented neural network.   
     
     
         15 . A system comprising one or more processor-based devices configured to:
 deliver automatically a first one or more recommendations, wherein the first one or more recommendations comprises a first one or more modifications to a computer-implemented system;   access automatically a first plurality of usage behaviors that are responsive to the first one or more recommendations;   apply automatically a computer-implemented neural network to analyze the first plurality of usage behaviors;   determine automatically one or more probabilities in accordance with the application of the computer-implemented neural network;   generate automatically a second one or more recommendations that are generated in accordance with the one or more probabilities, wherein the second one or more recommendations comprises a second one or more modifications of the computer-implemented system; and   deliver automatically the second one or more recommendations.   
     
     
         16 . The system of  claim 15 , further comprising the one or more processor-based devices configured to:
 access the first plurality of usage behaviors, wherein the first plurality of usage behaviors are associated with a plurality of users.   
     
     
         17 . The system of  claim 15 , further comprising the one or more processor-based devices configured to:
 determine automatically the one or more probabilities, wherein each of the one or more probabilities is associated with an inference of a preference.   
     
     
         18 . The system of  claim 15 , further comprising the one or more processor-based devices configured to:
 generate automatically the second one or more recommendations, wherein the second one or more recommendations are further generated in accordance with a value of information.   
     
     
         19 . The system of  claim 18 , further comprising the one or more processor-based devices configured to:
 generate further the second one or more recommendations in accordance with the value of information, wherein the value of information is determined by application of a monte carlo simulation.   
     
     
         20 . The system of  claim 15 , further comprising the one or more processor-based devices configured to:
 generate automatically the second one or more recommendations, wherein the second one or more recommendations are further generated in accordance with an automatic analysis of intrinsic patterns within content.