Content based related view recommendations

Abstract

Embodiments are directed to managing visualizations of data. A visualization associated with one or more community visualizations that are associated with an organization may be displayed. A recommendation score may be generated for each of the community visualizations based on a comparison of meta-data fields. Top ranked community visualizations may be determined based on the recommendation score associated with each of the community visualizations. An influence score may be determined the meta-data fields based on a proportion of a value each meta-data field contributes to the recommendation score of a corresponding top ranked community visualization. A report that includes a rank ordered list of the top ranked community visualizations and the top ranked other meta-data fields may be provided to the user.

What is claimed as new and desired to be protected by Letters Patent of the United States is: 
     
         1 . A method for managing visualizations of data using one or more processors that execute instructions to perform actions, comprising:
 displaying a visualization associated with one or more community visualizations that are associated with an organization, wherein a user is associated with the organization;   determining one or more fields of meta-data associated with the displayed visualization, wherein the one or more meta-data fields are hidden from view in the displayed visualization;   generating a recommendation score for each of the community visualizations based on a recommendation model and the one or more meta-data fields, wherein the recommendation score is based on a comparison of the one or more meta-data fields to one or more other meta-data fields associated with the one or more community visualizations;   determining a top ranking of the one or more community visualizations, wherein the top ranking is based on the recommendation score associated with each of the one or more community visualizations;   determining an influence score for each of the one or more other meta-data fields based on a proportion of a value each other meta-data field contributes to the recommendation score of a corresponding top ranked community visualization, wherein one or more top ranked other meta-data fields for each top ranked community visualization are based on the influence score for each of the one or more other meta-data fields; and   providing a report that includes a rank ordered list of the top ranked community visualizations and the one or more top ranked other meta-data fields.   
     
     
         2 . The method of  claim 1 , wherein determining the one or more meta-data fields, further comprises, determining the one or more meta-data fields from a plurality of meta-data fields based on one or more of a filter or a rule included in the recommendation model, wherein the plurality of meta-data fields include one or more of an author name, caption, visualization name, column name, table_name, data source name, author role, author location, author organization, reference to another table, data model object name, creation date, or last-accessed date. 
     
     
         3 . The method of  claim 1 , further comprising:
 providing one or more sub-models that include one or more of, one or more heuristics, one or more trained machine learning models, or one or more filters, wherein the one or more sub-models are included in the recommendation model;   generating one or more partial scores based on the one or more sub-models, the one or more meta-data fields, and the one or more other meta-data fields; and   determining the recommendation score for each community visualization based on a combination of the one or more partial scores, wherein the combination is provided by the recommendation model.   
     
     
         4 . The method of  claim 1 , further comprising:
 monitoring one or more actions of the user that are associated with the one or more top ranked community visualizations;   storing information associated with the one or more actions in a data store; and   updating the recommendation model based on the information stored in the data store.   
     
     
         5 . The method of  claim 1 , further comprising, associating a narrative with each top ranked community visualization based on its top ranked other meta-data fields, wherein the narrative includes a natural language explanation for a rank of each top ranked community visualization based on the one or more top ranked other meta-data fields associated with each top ranked community visualization. 
     
     
         6 . The method of  claim 1 , wherein determining the influence score for each of the one or more other meta-data fields, further comprises, employing one or more influence models included in the recommendation model to determine the influence score based on one or more of one or more dominant topics determine by a topic model, one or more counts of the one or more other meta-data fields that include common values, or a magnitude of change to the recommendation score and a temporary recommendation score that is generated based on a temporary omission of one of the one or more other meta-data fields from the generation of the temporary recommendation score, wherein the influence score for each other meta-data field is based on the magnitude of change that corresponds to its omission. 
     
     
         7 . The method of  claim 1 , wherein generating the recommendation score for each of the community visualizations based on the recommendation model and the one or more meta-data fields, further comprises, employing one or more machine learning actions to generate the recommendation score, wherein the one or more machine learning actions include one or more of Latent Semantic Analysis (LSA), Factorization Machines (FM), Cosine-similarity, Gradient Boosting Decision Trees (GBDTs), Term Frequency-Inverse Document Frequency (TF-IDF), or discriminant analysis. 
     
     
         8 . A processor readable non-transitory storage media that includes instructions for managing visualizations of data, wherein execution of the instructions by one or more processors, performs actions, comprising:
 displaying a visualization associated with one or more community visualizations that are associated with an organization, wherein a user is associated with the organization;   determining one or more fields of meta-data associated with the displayed visualization, wherein the one or more meta-data fields are hidden from view in the displayed visualization;   generating a recommendation score for each of the community visualizations based on a recommendation model and the one or more meta-data fields, wherein the recommendation score is based on a comparison of the one or more meta-data fields to one or more other meta-data fields associated with the one or more community visualizations;   determining a top ranking of the one or more community visualizations, wherein the top ranking is based on the recommendation score associated with each of the one or more community visualizations;   determining an influence score for each of the one or more other meta-data fields based on a proportion of a value each other meta-data field contributes to the recommendation score of a corresponding top ranked community visualization, wherein one or more top ranked other meta-data fields for each top ranked community visualization are based on the influence score for each of the one or more other meta-data fields; and   providing a report that includes a rank ordered list of the top ranked community visualizations and the one or more top ranked other meta-data fields.   
     
     
         9 . The media of  claim 8 , wherein determining the one or more meta-data fields, further comprises, determining the one or more meta-data fields from a plurality of meta-data fields based on one or more of a filter or a rule included in the recommendation model, wherein the plurality of meta-data fields include one or more of an author name, caption, visualization name, column name, table_name, data source name, author role, author location, author organization, reference to another table, data model object name, creation date, or last-accessed date. 
     
     
         10 . The media of  claim 8 , further comprising:
 providing one or more sub-models that include one or more of, one or more heuristics, one or more trained machine learning models, or one or more filters, wherein the one or more sub-models are included in the recommendation model;   generating one or more partial scores based on the one or more sub-models, the one or more meta-data fields, and the one or more other meta-data fields; and   determining the recommendation score for each community visualization based on a combination of the one or more partial scores, wherein the combination is provided by the recommendation model.   
     
     
         11 . The media of  claim 8 , further comprising:
 monitoring one or more actions of the user that are associated with the one or more top ranked community visualizations;   storing information associated with the one or more actions in a data store; and   updating the recommendation model based on the information stored in the data store.   
     
     
         12 . The media of  claim 8 , further comprising, associating a narrative with each top ranked community visualization based on its top ranked other meta-data fields, wherein the narrative includes a natural language explanation for a rank of each top ranked community visualization based on the one or more top ranked other meta-data fields associated with each top ranked community visualization. 
     
     
         13 . The media of  claim 8 , wherein determining the influence score for each of the one or more other meta-data fields, further comprises, employing one or more influence models included in the recommendation model to determine the influence score based on one or more of one or more dominant topics determine by a topic model, one or more counts of the one or more other meta-data fields that include common values, or a magnitude of change to the recommendation score and a temporary recommendation score that is generated based on a temporary omission of one of the one or more other meta-data fields from the generation of the temporary recommendation score, wherein the influence score for each other meta-data field is based on the magnitude of change that corresponds to its omission. 
     
     
         14 . The media of  claim 8 , wherein generating the recommendation score for each of the community visualizations based on the recommendation model and the one or more meta-data fields, further comprises, employing one or more machine learning actions to generate the recommendation score, wherein the one or more machine learning actions include one or more of Latent Semantic Analysis (LSA), Factorization Machines (FM), Cosine-similarity, Gradient Boosting Decision Trees (GBDTs), Term Frequency-Inverse Document Frequency (TF-IDF), or discriminant analysis. 
     
     
         15 . A system for managing visualizations of data:
 a network computer, comprising:
 a memory that stores at least instructions; and 
 one or more processors that execute instructions that perform actions, including:
 displaying a visualization associated with one or more community visualizations that are associated with an organization, wherein a user is associated with the organization; 
 determining one or more fields of meta-data associated with the displayed visualization, wherein the one or more meta-data fields are hidden from view in the displayed visualization; 
 generating a recommendation score for each of the community visualizations based on a recommendation model and the one or more meta-data fields, wherein the recommendation score is based on a comparison of the one or more meta-data fields to one or more other meta-data fields associated with the one or more community visualizations; 
 determining a top ranking of the one or more community visualizations, wherein the top ranking is based on the recommendation score associated with each of the one or more community visualizations; 
 determining an influence score for each of the one or more other meta-data fields based on a proportion of a value each other meta-data field contributes to the recommendation score of a corresponding top ranked community visualization, wherein one or more top ranked other meta-data fields for each top ranked community visualization are based on the influence score for each of the one or more other meta-data fields; and 
 providing a report that includes a rank ordered list of the top ranked community visualizations and the one or more top ranked other meta-data fields; and 
 
   a client computer, comprising:
 a memory that stores at least instructions; and 
 one or more processors that execute instructions that perform actions, including:
 receiving the report. 
 
   
     
     
         16 . The system of  claim 15 , wherein determining the one or more meta-data fields, further comprises, determining the one or more meta-data fields from a plurality of meta-data fields based on one or more of a filter or a rule included in the recommendation model, wherein the plurality of meta-data fields include one or more of an author name, caption, visualization name, column name, table_name, data source name, author role, author location, author organization, reference to another table, data model object name, creation date, or last-accessed date. 
     
     
         17 . The system of  claim 15 , wherein the one or more processors of the network computer execute instructions that perform actions, further comprising:
 providing one or more sub-models that include one or more of, one or more heuristics, one or more trained machine learning models, or one or more filters, wherein the one or more sub-models are included in the recommendation model;   generating one or more partial scores based on the one or more sub-models, the one or more meta-data fields, and the one or more other meta-data fields; and   determining the recommendation score for each community visualization based on a combination of the one or more partial scores, wherein the combination is provided by the recommendation model.   
     
     
         18 . The system of  claim 15 , wherein the one or more processors of the network computer execute instructions that perform actions, further comprising:
 monitoring one or more actions of the user that are associated with the one or more top ranked community visualizations;   storing information associated with the one or more actions in a data store; and   updating the recommendation model based on the information stored in the data store.   
     
     
         19 . The system of  claim 15 , wherein the one or more processors of the network computer execute instructions that perform actions, further comprising, associating a narrative with each top ranked community visualization based on its top ranked other meta-data fields, wherein the narrative includes a natural language explanation for a rank of each top ranked community visualization based on the one or more top ranked other meta-data fields associated with each top ranked community visualization. 
     
     
         20 . The system of  claim 15 , wherein determining the influence score for each of the one or more other meta-data fields, further comprises, employing one or more influence models included in the recommendation model to determine the influence score based on one or more of one or more dominant topics determine by a topic model, one or more counts of the one or more other meta-data fields that include common values, or a magnitude of change to the recommendation score and a temporary recommendation score that is generated based on a temporary omission of one of the one or more other meta-data fields from the generation of the temporary recommendation score, wherein the influence score for each other meta-data field is based on the magnitude of change that corresponds to its omission. 
     
     
         21 . The system of  claim 15 , wherein generating the recommendation score for each of the community visualizations based on the recommendation model and the one or more meta-data fields, further comprises, employing one or more machine learning actions to generate the recommendation score, wherein the one or more machine learning actions include one or more of Latent Semantic Analysis (LSA), Factorization Machines (FM), Cosine-similarity, Gradient Boosting Decision Trees (GBDTs), Term Frequency-Inverse Document Frequency (TF-IDF), or discriminant analysis. 
     
     
         22 . A network computer for managing visualizations of data, comprising:
 a memory that stores at least instructions; and   one or more processors that execute instructions that perform actions, including:
 displaying a visualization associated with one or more community visualizations that are associated with an organization, wherein a user is associated with the organization; 
 determining one or more fields of meta-data associated with the displayed visualization, wherein the one or more meta-data fields are hidden from view in the displayed visualization; 
 generating a recommendation score for each of the community visualizations based on a recommendation model and the one or more meta-data fields, wherein the recommendation score is based on a comparison of the one or more meta-data fields to one or more other meta-data fields associated with the one or more community visualizations; 
 determining a top ranking of the one or more community visualizations, wherein the top ranking is based on the recommendation score associated with each of the one or more community visualizations; 
 determining an influence score for each of the one or more other meta-data fields based on a proportion of a value each other meta-data field contributes to the recommendation score of a corresponding top ranked community visualization, wherein one or more top ranked other meta-data fields for each top ranked community visualization are based on the influence score for each of the one or more other meta-data fields; and 
 providing a report that includes a rank ordered list of the top ranked community visualizations and the one or more top ranked other meta-data fields. 
   
     
     
         23 . The network computer of  claim 22 , wherein determining the one or more meta-data fields, further comprises, determining the one or more meta-data fields from a plurality of meta-data fields based on one or more of a filter or a rule included in the recommendation model, wherein the plurality of meta-data fields include one or more of an author name, caption, visualization name, column name, table_name, data source name, author role, author location, author organization, reference to another table, data model object name, creation date, or last-accessed date. 
     
     
         24 . The network computer of  claim 22 , wherein the one or more processors execute instructions that perform actions, further comprising:
 providing one or more sub-models that include one or more of, one or more heuristics, one or more trained machine learning models, or one or more filters, wherein the one or more sub-models are included in the recommendation model;   generating one or more partial scores based on the one or more sub-models, the one or more meta-data fields, and the one or more other meta-data fields; and   determining the recommendation score for each community visualization based on a combination of the one or more partial scores, wherein the combination is provided by the recommendation model.   
     
     
         25 . The network computer of  claim 22 , wherein the one or more processors execute instructions that perform actions, further comprising:
 monitoring one or more actions of the user that are associated with the one or more top ranked community visualizations;   storing information associated with the one or more actions in a data store; and   updating the recommendation model based on the information stored in the data store.   
     
     
         26 . The network computer of  claim 22 , wherein the one or more processors execute instructions that perform actions, further comprising, associating a narrative with each top ranked community visualization based on its top ranked other meta-data fields, wherein the narrative includes a natural language explanation for a rank of each top ranked community visualization based on the one or more top ranked other meta-data fields associated with each top ranked community visualization. 
     
     
         27 . The network computer of  claim 22 , wherein determining the influence score for each of the one or more other meta-data fields, further comprises, employing one or more influence models included in the recommendation model to determine the influence score based on one or more of one or more dominant topics determine by a topic model, one or more counts of the one or more other meta-data fields that include common values, or a magnitude of change to the recommendation score and a temporary recommendation score that is generated based on a temporary omission of one of the one or more other meta-data fields from the generation of the temporary recommendation score, wherein the influence score for each other meta-data field is based on the magnitude of change that corresponds to its omission. 
     
     
         28 . The network computer of  claim 22 , wherein generating the recommendation score for each of the community visualizations based on the recommendation model and the one or more meta-data fields, further comprises, employing one or more machine learning actions to generate the recommendation score, wherein the one or more machine learning actions include one or more of Latent Semantic Analysis (LSA), Factorization Machines (FM), Cosine-similarity, Gradient Boosting Decision Trees (GBDTs), Term Frequency-Inverse Document Frequency (TF-IDF), or discriminant analysis.