Abstract:
A system includes: a preference model generating unit that generates a preference model by learning and storing the preference model for a user based on a behavior history that indicates history of behavior of the user; an evaluation list generating unit that calculates probability of a plurality of recommended candidates based on the preference model and generates an evaluation list indicating the probability of the recommended candidates; a default predicting unit that specifies habituation of the user based on the information on the behavior history and calculates a predicted default value of the plurality of recommended candidates based on the habituation; and an evaluated unexpectedness value calculating unit that calculates an evaluated unexpectedness value indicating unexpectedness where the unexpectedness is higher as a difference obtained by subtracting the predicted default value from the probability is larger.

Description:
RELATED APPLICATION(S) 
       [0001]    The present disclosure relates to the subject matter contained in Japanese Patent Application No. 2007-104804 filed on Apr. 12, 2007, which is incorporated herein by reference in its entirety. 
       FIELD 
       [0002]    The present invention relates to an information evaluation system and a method for evaluating information, which can be used for evaluating various items of daily goods, such as groceries and books, broadcast contents, such as movies and music, while allowing to evaluate unexpectedness of a recommended result. 
       BACKGROUND 
       [0003]    As disclosed in a related-art document [1], which is listed below, indicators such as “precision,” “aveP,” and “recall” described below are known as an indicator indicating performance of an information estimation system. 
         [0000]    
       
         
           
             
               
                 
                   precision 
                   = 
                   
                     
                       count 
                        
                       
                         ( 
                         L 
                         ) 
                       
                     
                     / 
                     L 
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
             
               
                 
                   aveP 
                   = 
                   
                     
                       1 
                       R 
                     
                      
                     
                       
                         ∑ 
                         
                           1 
                           ≤ 
                           i 
                           ≤ 
                           L 
                         
                       
                        
                       
                         
                           isrel 
                            
                           
                             ( 
                             
                               s 
                               i 
                             
                             ) 
                           
                         
                         · 
                         
                           
                             count 
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                               ( 
                               i 
                               ) 
                             
                           
                           i 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
             
               
                 
                   recall 
                   = 
                   
                     
                       count 
                        
                       
                         ( 
                         L 
                         ) 
                       
                     
                     / 
                     R 
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
         [0004]    In the equations (1)-(3), L represents the number of recommended items, R represents a total number of suitable items, i represents an i-th ranked recommended item, isrel(s i ) represents a degree of suitability of s i  for a user&#39;s interest, and count(i) represents the number of suitable items of top i-items in the ranking. 
         [0005]    The “precision” indicates a ratio of items (count(L)) suitable for a user&#39;s preference occupied in a recommending list. “aveP” indicates an average value of “precision” of the items (isrel(Si)) suitable for the user&#39;s preference in consideration of the ranks. The “recall” indicates a ratio of the items suitable for the user&#39;s preference to the entire suitable items (R). By using the indicators such as precision, aveP, and recall, it is possible to measure the degree of suitability of the recommended result for the user&#39;s preference. 
       LIST OF RELATED-ART DOCUMENTS 
       [0000]    
       
         C. Cleverdon and M. Kean “Factors Determining the Performance of Indexing Systems” Aslib Cranfield Research Project, Cranfield England (1968) [1] 
         R. J. Quinlan: C4. 5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, Calif. (1993) [2] 
         C. M. Bishop: Neural Networks for Pattern Recognition, Oxford University Press (1995) [3] 
         V. N. Vapnik: The Nature of Statistical Learning Theory, Springer (1995) [4] 
       
     
         [0010]    In information estimation systems, usefulness of the information estimation system is reduced and the use thereof does not last when the recommended result is information already known to the user or when the user does not feel surprise with the recommended result. There is a need for an information estimation system that provides an unexpected recommended result that suits for the user&#39;s preference to allow the use thereof to last without damaging the user&#39;s reliability. 
       SUMMARY 
       [0011]    According to a first aspect of the invention, there is provided an information evaluation system including: a preference model generating unit that generates a preference model by learning and storing the preference model for a user based on a behavior history that indicates history of behavior of the user; an evaluation list generating unit that calculates probability of a plurality of recommended candidates based on the preference model and generates an evaluation list indicating the probability of the recommended candidates; a default predicting unit that specifies habituation of the user based on the information on the behavior history and calculates a predicted default value of the plurality of recommended candidates based on the habituation; and an evaluated unexpectedness value calculating unit that calculates an evaluated unexpectedness value indicating unexpectedness where the unexpectedness is higher as a difference obtained by subtracting the predicted default value from the probability is larger. 
         [0012]    According to a second aspect of the invention, there is provided a method for evaluating information on a recommended candidate to a user, the method including: generating a preference model by learning and storing the preference model for the user based on a behavior history that indicates history of behavior of the user; calculating probability of a plurality of recommended candidates based on the preference model; generating an evaluation list indicating the probability of the recommended candidates; specifying habituation of the user based on the information on the behavior history; calculating a predicted default value of the plurality of recommended candidates based on the habituation; and calculating an evaluated unexpectedness value indicating unexpectedness where the unexpectedness is higher as a difference obtained by subtracting the predicted default value from the probability is larger. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0013]    In the accompanying drawings: 
           [0014]      FIG. 1  is a block diagram illustrating an entire configuration of an information estimation system according to an embodiment of the present invention; 
           [0015]      FIG. 2  is a flowchart illustrating an entire procedure of the information estimation system shown in  FIG. 1 ; 
           [0016]      FIG. 3  is a flowchart illustrating details of a preference model preparing step (step A) of the procedure shown in  FIG. 2 ; 
           [0017]      FIG. 4  is a flowchart illustrating details of an evaluation list step (step B) of the procedure shown in  FIG. 2 ; 
           [0018]      FIG. 5  is a flowchart illustrating details of a default predicting step (step C) of the procedure shown in  FIG. 2 ; 
           [0019]      FIG. 6  is a flowchart illustrating details of an evaluated unexpectedness value (unexpectedness) calculating step (step D) of the procedure shown in  FIG. 2 ; 
           [0020]      FIG. 7  is a flowchart illustrating details of an evaluated unexpectedness value (unexpectedness_r) calculating step (step D) of the procedure shown in  FIG. 2 ; 
           [0021]      FIG. 8  is a flowchart illustrating details of a recommendation list preparing step (step E) of the procedure shown in  FIG. 2 ; 
           [0022]      FIG. 9  is a table illustrating an example of a viewer&#39;s viewing history information on television program information ( FIG. 10 ) in the information estimation system shown in  FIG. 1 ; 
           [0023]      FIG. 10  is a table illustrating an example of television program information in the information estimation system shown in  FIG. 1 ; 
           [0024]      FIG. 11  is a diagram illustrating a description example of defining a preference model structure in the information estimation system shown in  FIG. 1 ; 
           [0025]      FIG. 12  is a table illustrating an example of a conditional probability value of a preference model in the information estimation system shown in  FIG. 1 ; 
           [0026]      FIG. 13  is a table illustrating an example of an evaluation program list to be prepared in the information estimation system shown in  FIG. 1 ; 
           [0027]      FIG. 14  is a table illustrating an example of a default prediction result to be used in the information estimation system shown in  FIG. 1 ; 
           [0028]      FIG. 15  is a table illustrating an example of interest data to be used in the information estimation system shown in  FIG. 1 ; 
           [0029]      FIG. 16  is a table illustrating an example of an evaluated-value program database to be prepared in the information estimation system shown in  FIG. 1 ; and 
           [0030]      FIG. 17  is a table illustrating an example of an evaluation program list to be prepared in the information to estimation system shown in  FIG. 1 . 
       
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
       [0031]    Hereinafter, an information evaluation system according to an embodiment of the invention will be described with reference to the drawings. 
         [0032]    Although configurations and processes of an information evaluation system that evaluates and recommends broadcast programs to a user will be described in this embodiment, the present invention is not limited to a system that evaluates an item of broadcast programs, but can be applied to all kinds of items of daily goods, such as groceries and books, broadcast contents, such as movies and music, and the like. It is noted that the present invention is not only useful for evaluating the broadcast programs according to this embodiment but is also useful for evaluating recommended results of any information by measuring unexpectedness of the recommended results belonging to the present invention. 
         [0033]      FIG. 1  shows a configuration of the information evaluation system according to the embodiment of the present invention.  FIG. 2  is a flowchart illustrating a processing procedure of the information estimation system according to the embodiment.  FIGS. 3 to 5  and  FIG. 8  are flowcharts illustrating details of steps A, B, C, and E shown in  FIG. 2 .  FIGS. 6 and 7  are two types of flowcharts illustrating details of step D shown in  FIG. 2 . 
         [0034]    The information estimation system according to the embodiment uses a broadcast receiver  1  for receiving and telecasting broadcast programs, an example of which is shown in  FIG. 1 . The broadcast receiver  1  is connected to a broadcasting server  41  through a network  40  by wired or wireless connection. A broadcast contents receiver  2  receives program contents and telecasts the program contents to users by the use of a program telecasting device  5 . 
         [0035]    The broadcast contents receiver  2  of the broadcasting terminal  1  receives program data (i.e. EPG: Electronic Program Guide). A program data manager  6  stores the received program data in a program database  3  and manages the data such as periodically updating the data. An operation history collecting device  14  collects a user&#39;s television operation history information by a remote controller or a broadcast channel changing device. A viewing history manager  7  stores the collected television operation history information in a viewing history database  4  and manages the data such as periodically updating the data. The program operation history information is collected, for example, as the data shown in  FIG. 9 . 
         [0036]      FIG. 9  shows that a viewer viewed a program having a title “dinner of today” of which an attribute “viewing” is a value of “TRUE”. The viewing history information is not limited to the contents shown in  FIG. 9 , but may be information having various formats or definitions and may be input in the form of input using a keyboard, input using a remote controller, on-line input through a network, and reading from a magnetic tape which is an information delivery medium. 
         [0037]    The schematic processing procedure of the information estimation system is described now with reference to  FIG. 2 . First, a viewing history data reading step is performed (step S 1 ) and then a program data reading step is performed (step S 2 ). Next, a preference model preparing step (step A), an evaluation list preparing step (step B), a default predicting step (step C), a predicted unexpectedness value calculating step (step D), and a recommending list preparing step (step E) are sequentially performed. Thereafter, a recommending list arranging step (step S 3 ) and an interest data collecting step (step S 4 ) are performed. 
         [0038]    The step (step A) of expressing and preparing a preference model using a Bayesian network is performed mainly by the use of a preference model generating unit  45  of the broadcast receiver  1  shown in  FIG. 1 . The preference model generating unit  45  includes a preference model learning unit  8 , a preference model manager  9 , and a preference model database  10 . 
         [0039]    The process shown in  FIG. 3  is performed in the preference model preparing step A. The preference model learning unit  8  reads a structure defining data for defining a preference model structure as shown in  FIG. 11  (step A 1 ). The preference model learning unit  8  reads program information for a predetermined period in the past from a program data manager  6  as shown in  FIG. 10  (step A 2 ). The preference model learning unit  8  reads the viewing history information for a predetermined past period from a viewing history manager  7  as shown in  FIG. 9  (step A 3 ). The preference model learning unit  8  calculates a conditional probability value of probability variables in the Bayesian network as shown in  FIG. 12  (step A 4 ). The conditional probability value can be calculated as a frequency of a suitable program from the viewing history information shown in  FIG. 9  or can be obtained by allowing a system designer to set a value. The data defining the obtained network structure and the conditional probability value are stored and managed as a preference model in the preference model database  10  (step A 5 ). Although the preference model is expressed and prepared using the Bayesian network in this embodiment, the preference model may be learned using other methods such as a decision tree as described in the related-art document [2]. 
         [0040]    The evaluation list preparing step B is performed by the procedure shown in  FIG. 4  by the use of an evaluation list generating unit  46 . The evaluation list generating unit  46  includes a viewing probability calculating unit  15 , a sorting unit  16 , a program selector  17 , an evaluation list database  18 , and a viewing probability value database  19 . 
         [0041]    The viewing probability calculator  15  reads the program information managed by the program data manager  6  (step B 1 ). The viewing probability calculator  15  reads the preference model managed by the preference model manager  9  (step B 2 ). Specifically, a file defining the preference model structure shown in  FIG. 11  or a file defining the conditional probability value of the probability variables shown in  FIG. 12  is read by a computer. The preference model is not limited to the above-mentioned ones, but may be input in the form of input using a keyboard, or input using a remote controller, on-line input through a network, and reading from a magnetic tape as an information delivery medium. 
         [0042]    The viewing probability calculator  15  calculates a viewing probability value of objective programs using the preference model and the program data as input information (step B 3 ). The viewing probability value can be obtained by the probability inference using the preference model. For example, when a program genre of a television program to be broadcast in the future is “variety”, a viewer&#39;s viewing probability P(viewing=TRUE|program_genre=variety) of the program is calculated using the probability distribution of conditional probability values shown in  FIG. 12  by the following expression. 
         [0000]    
       
         
           
             
               
                 
                   
                     P 
                      
                     
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                         viewing 
                         = 
                         
                           
                             TRUE 
                             | 
                             program_genre 
                           
                           = 
                           variety 
                         
                       
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                           = 
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                                 | 
                                 viewing 
                               
                               = 
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                             program_genre 
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         [0043]    The sorting unit  16  sorts the program data having a viewing probability value given thereto in a descending order of the viewing probability values (step B 4 ). The program selector  17  stores upper X programs of the program data sorted by the sorting unit  16  as evaluated programs in the evaluation list database  18  and stores the sorted program data in the viewing probability value database  19  (step B 5 ). 
         [0044]    The program data having a viewing probability value given thereto which is stored in the evaluation list database  18  or the viewing probability value database  19  has the format shown in  FIG. 13 . Although the preference model is learned and the evaluated program is selected on the basis on the learned preference model in this embodiment, the preference model may be learned using other methods of a decision tree as described in the related-art document [2] or the evaluation program list including a user&#39;s favorite programs may be prepared by classifying the program data into a user&#39;s favorite program data and the other program data using a classifier such as a neural network as described in the related-art document [3] or an SVM as described in the related-art document [4]. 
         [0045]    The default predicting step C is performed by the procedure shown in  FIG. 5  by the use of a default predicting unit  47 . The default predicting unit  47  includes a habituation specifying unit  11 , a viewing predicting unit  12 , and a default prediction result database  13 . 
         [0046]    The viewing probability calculator  15  reads the program information managed by the program data manager  6  (step C 1 ). The habituation specifying unit  11  reads the viewing history information managed by the viewing history manager  7  (step C 2 ). The habituation specifying unit  11  specifies a program, which is habitually viewed by a viewer, based on the program data and the viewing history (step C 3 ). In specifying the program which is habitually viewed, a program having the same title or broadcast time as the program viewed in the past can be specified as the program which is habitually viewed. The viewing predicting unit  12  predicts (default-predicts) the viewing based on the program information determined as being habitually viewed and the program information managed by the program data manager  6  (step C 4 ). 
         [0047]    The viewing predicting unit  12  predicts the viewing prediction result of programs as shown in  FIG. 14 . In  FIG. 14 , as the default prediction result, “1” is given to the program predicted as the viewing and “0” is given to the program not predicted as the viewing. The values are not limited to 1 and 0, but may be a decimal value of [0;1]. The viewing prediction result of the program predicted by the viewing predicting unit  12  is stored in the default prediction result database  13  (step C 4 ). 
         [0048]    The evaluated unexpectedness value calculating step D is performed by an evaluated unexpectedness value calculating unit  48 . The evaluated unexpectedness value calculating unit  48  includes a prediction result comparing unit  20 , an item counter  21 , an evaluation adder  22 , an evaluation divider  23 , an evaluated value output unit  24 , an evaluation database  25 , and an interest database  26 . In the evaluated unexpectedness value calculating step D, the calculation of “unexpectedness” shown in  FIG. 6  and the calculation of “unexpectedness_r” show in  FIG. 7  are performed. 
         [0049]    The prediction result comparing unit  20  reads the evaluation list from the evaluation list database  18 , reads the default prediction result from the default prediction result database  13 , reads the interest data from the interest database  26 , and the viewing probability value data from the viewing probability value database  19  (step D 1  and step D 1 ′). For example, the evaluation list is shown in  FIG. 13 , the interest data is shown in  FIG. 15 , and the default prediction result is shown in  FIG. 14 . As shown in  FIG. 15 , in the interest data, “TRUE” is given to the programs interested by a user and “FALSE” is given to the programs not interested by the user. The interest data may be collected by allowing the user to directly input the data by the use of an input device such as a keyboard or a mouse or the interest may be automatically determined using the past viewing history information and the like. 
         [0050]    The item counter  21  selects a program (program A) from the evaluation list (step D 2  and step D 2 ′) and determines whether program A is an interested program with reference to the interest database  26  (step D 3  and step D 3 ′). When it is determined that program A is the interested program, the predicted default value of program A is compared with the viewing probability value thereof (step D 4  and step D 4 ′). 
         [0051]    When the viewing probability value of program A is larger than the predicted default value, the evaluation adder  22  adds a difference between the viewing probability value and the predicted default value to the evaluated unexpectedness value “unexpectedness” (step D 5 ). Alternatively, when the viewing probability value of program A is larger than the predicted default value, the evaluation adder  22  counts the number of items in which the interested programs are included in the programs higher than the rank of program A in the evaluation list (step D 5 ′) and adds a product of the counted value and the difference between the viewing probability value and the predicted default value to the evaluated unexpectedness value “unexpectedness_r” (step D 6 ′). 
         [0052]    When the item counter  21  determines that program A is not the interested program and the evaluation adder  22  determines that the viewing probability value of program A is smaller than the predicted default value, the item counter  21  selects and evaluates another program from the evaluation list. 
         [0053]    When the processes (steps D 2  to D 5  and steps D 2 ′ to D 6 ′) are performed on all the programs in the evaluation list (step D 6  and step D 7 ′), the evaluation divider  23  divides the resultant by the number of items in the evaluation list (step D 7  and step D 8 ′). When the processes are not performed on all the programs in the evaluation list (step D 6  and step D 7 ′), the item counter  21  selects and evaluates another program in the evaluation list. The evaluated value output unit  24  stores the calculated evaluated value in the evaluation database  25 . 
         [0054]      FIG. 16  shows an example of evaluated-value program data. The evaluated values of “unexpectedness” and “unexpectedness_r” are calculated by the following expression. 
         [0000]    
       
         
           
             
               
                 
                   unexpectedness 
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                       L 
                     
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                   unexpectedness_r 
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         [0055]    In the equations (5) and (6), L represents the number of recommended items, s i  represents the i-th ranked recommended item, P(s i ) represents a prediction probability value of s i , d(s i ) represents the prediction probability value of s i  by the default prediction, isrel(s i ) represents a degree of suitability of s i  for the user&#39;s interest, and count(i) represents the number of suitable items of top i-items in the ranking. 
         [0056]    The recommended list preparing step (step E) is performed by the procedure shown in  FIG. 8  by the use of the recommended program determining unit  27 . The recommended program determining unit  27  reads the evaluation data from the evaluation database  25 , reads the viewing probability value data from the viewing probability value database  19  (step E 1 ), and reads the interest data from the interest database  26  (step E 3 ). 
         [0057]    The recommended program determining unit  27  selects only programs of which the viewing probability value is larger than the predicted default value and adds the selected programs to the recommending list (step E 2 ). The recommended program determining unit  27  sorts the program data in the order of viewing probability values (step E 4 ) and selects one program (program C) having the highest viewing probability value (step E 5 ). When program C is the interested program with reference to the interest data (step E 6 ), the recommended program determining unit compares the predicted default value of program C with the viewing probability value (step E 7 ). When the predicted default value is larger than the viewing probability value, recommended program determining unit adds program C to the recommending list (step E 8 ). When program C is not the interested program or the predicted default value of program C is smaller than the viewing probability value, the recommended program determining unit selects a program having a secondly-higher viewing probability value from the viewing probability values arranged in the descending order. When the size of the recommending list reaches N, the procedure ends. When the size is less than N, the recommended program determining unit selects a program having a next-higher viewing probability value from the viewing probability values arranged in the descending order. The recommended program determining unit  27  transmits the recommending list shown in  FIG. 17  to the program recommending device  28  (presentation unit) and the program recommending device  28  displays the recommending list on the broadcast receiver. 
         [0058]    It is to be understood that the present invention is not limited to the specific embodiment described above and that the invention can be embodied with the components modified without departing from the spirit and scope of the invention. The invention can be embodied in various forms according to appropriate combinations of the components disclosed in the embodiment described above. For example, some components may be deleted from all components shown in the embodiment. Further, the components in different embodiments may be used appropriately in combination.