Ranking data objects using machine learning and scoring to configure a graphical interface

Abstract

In general, embodiments of the present invention provide for ranking data objects using machine learning and scoring to configure a graphical interface. In this regard, a machine learning model is applied to data object attributes to generate data object relevance scores for the candidate data objects. Additionally, a respective personalization score for the candidate data objects is generated based at least in part on a compounded ratio and a geometric mean of the compounded ratio. The data object relevance scores for the candidate data objects are modified based at least in part on the respective personalization score for the candidate data objects to generate modified object relevance scores for the candidate data objects. The candidate data objects are then ranked based at least in part on the modified object relevance scores to generate a ranked data object set and to facilitate configuration of a graphical interface.

What is claimed is: 
     
         1 . An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to at least:
 receive a search query associated with data object attributes for candidate data objects to facilitate rendering of a graphical interface via a computing device;   apply a machine learning model to the data object attributes to generate data object relevance scores for the candidate data objects, wherein a respective data object relevance score of the data object relevance scores represents a real-time prediction as to whether an interaction with respect to a respective candidate data object rendered via the graphical interface via the computing device will satisfy one or more defined interaction rules;   generate a compounded ratio based at least in part on a combination of first graphical interface engagement signals associated with the computing device and second graphical interface engagement signals associated with one or more other computing devices;   generate a respective personalization score for the candidate data objects based at least in part on the compounded ratio and a geometric mean of the compounded ratio;   modify the data object relevance scores for the candidate data objects based at least in part on the respective personalization score for the candidate data objects to generate modified object relevance scores for the candidate data objects;   rank the candidate data objects based at least in part on the modified object relevance scores to generate a ranked data object set;   configure the graphical interface based at least in part on the ranked data object set to render one or more visual display elements associated with the ranked data object set; and   transmit the graphical interface to the computing device.   
     
     
         2 . The apparatus of  claim 1 , wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the apparatus to:
 subsequent transmission of the graphical interface to the computing device, receive one or more graphical interface engagement signals with respect to the one or more visual display elements.   
     
     
         3 . The apparatus of  claim 2 , wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the apparatus to:
 update the machine learning model based at least in part on the one or more graphical interface engagement signals.   
     
     
         4 . The apparatus of  claim 1 , wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the apparatus to:
 retrieve, from a data repository, one or more user identifier attributes for a user identifier associated with the search query; and   apply the machine learning model to the data object attributes and the one or more user identifier attributes to generate the data object relevance scores.   
     
     
         5 . The apparatus of  claim 1 , wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the apparatus to:
 retrieve, from a data repository, one or more data objects event attributes for one or more data object events with respect to historical visual display elements; and   apply the machine learning model to the data object attributes and the one or more data objects event attributes to generate the data object relevance scores.   
     
     
         6 . The apparatus of  claim 1 , wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the apparatus to:
 retrieve one or more other data object attributes based at least in part on a query of an attribute graph using at least a portion of the data object attributes; and   apply the machine learning model to the data object attributes and the one or more other data object attributes to generate the data object relevance scores.   
     
     
         7 . The apparatus of  claim 1 , wherein the computing device is a mobile computing device and the graphical interface is a real-time graphical interface with respect to the search query, and wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the apparatus to:
 cause rendering of the one or more visual display elements via the real-time graphical interface of the mobile computing device.   
     
     
         8 . A computer-implemented method, comprising:
 receiving, by a computing device comprising a processor, a search query associated with data object attributes for candidate data objects to facilitate rendering of a graphical interface via a computing device;   applying, by the computing device, a machine learning model to the data object attributes to generate data object relevance scores for the candidate data objects, wherein a respective data object relevance score of the data object relevance scores represents a real-time prediction as to whether an interaction with respect to a respective candidate data object rendered via the graphical interface via the computing device will satisfy one or more defined interaction rules;   generating, by the computing device, a compounded ratio based at least in part on a combination of first graphical interface engagement signals associated with the computing device and second graphical interface engagement signals associated with one or more other computing devices;   generating, by the computing device, a respective personalization score for the candidate data objects based at least in part on the compounded ratio and a geometric mean of the compounded ratio;   modifying, by the computing device, the data object relevance scores for the candidate data objects based at least in part on the respective personalization score for the candidate data objects to generate modified object relevance scores for the candidate data objects;   ranking, by the computing device, the candidate data objects based at least in part on the modified object relevance scores to generate a ranked data object set;   configuring, by the computing device, the graphical interface based at least in part on the ranked data object set to render one or more visual display elements associated with the ranked data object set; and   transmitting, by the computing device, the graphical interface to the computing device.   
     
     
         9 . The computer-implemented method of  claim 8 , further comprising:
 subsequent transmission of the graphical interface to the computing device, receiving, by the computing device, one or more graphical interface engagement signals with respect to the one or more visual display elements.   
     
     
         10 . The computer-implemented method of  claim 9 , further comprising:
 updating, by the computing device, the machine learning model based at least in part on the one or more graphical interface engagement signals.   
     
     
         11 . The computer-implemented method of  claim 8 , further comprising:
 retrieving, by the computing device and from a data repository, one or more user identifier attributes for a user identifier associated with the search query; and   applying, by the computing device, the machine learning model to the data object attributes and the one or more user identifier attributes to generate the data object relevance scores.   
     
     
         12 . The computer-implemented method of  claim 8 , further comprising:
 retrieving, by the computing device and from a data repository, one or more data objects event attributes for one or more data object events with respect to historical visual display elements; and   applying, by the computing device, the machine learning model to the data object attributes and the one or more data objects event attributes to generate the data object relevance scores.   
     
     
         13 . The computer-implemented method of  claim 8 , further comprising:
 retrieving, by the computing device, one or more other data object attributes based at least in part on a query of an attribute graph using at least a portion of the data object attributes; and   applying, by the computing device, the machine learning model to the data object attributes and the one or more other data object attributes to generate the data object relevance scores.   
     
     
         14 . The computer-implemented method of  claim 8 , wherein the computing device is a mobile computing device and the graphical interface is a real-time graphical interface with respect to the search query, and the computer-implemented method of  claim 8  further comprising:
 causing, by the computing device, rendering of the one or more visual display elements via the real-time graphical interface of the mobile computing device. 
 
     
     
         15 . A computer program product, stored on a computer readable medium, comprising instructions that when executed by one or more computers cause the one or more computers to:
 receive a search query associated with data object attributes for candidate data objects to facilitate rendering of a graphical interface via a computing device;   apply a machine learning model to the data object attributes to generate data object relevance scores for the candidate data objects, wherein a respective data object relevance score of the data object relevance scores represents a real-time prediction as to whether an interaction with respect to a respective candidate data object rendered via the graphical interface via the computing device will satisfy one or more defined interaction rules;   generate a compounded ratio based at least in part on a combination of first graphical interface engagement signals associated with the computing device and second graphical interface engagement signals associated with one or more other computing devices;   generate a respective personalization score for the candidate data objects based at least in part on the compounded ratio and a geometric mean of the compounded ratio;   modify the data object relevance scores for the candidate data objects based at least in part on the respective personalization score for the candidate data objects to generate modified object relevance scores for the candidate data objects;   rank the candidate data objects based at least in part on the modified object relevance scores to generate a ranked data object set;   configure the graphical interface based at least in part on the ranked data object set to render one or more visual display elements associated with the ranked data object set; and   transmit the graphical interface to the computing device.   
     
     
         16 . The computer program product of  claim 15 , further comprising instructions that when executed by the one or more computers cause the one or more computers to:
 subsequent transmission of the graphical interface to the computing device, receive one or more graphical interface engagement signals with respect to the one or more visual display elements.   
     
     
         17 . The computer program product of  claim 16 , further comprising instructions that when executed by the one or more computers cause the one or more computers to:
 update the machine learning model based at least in part on the one or more graphical interface engagement signals.   
     
     
         18 . The computer program product of  claim 15 , further comprising instructions that when executed by the one or more computers cause the one or more computers to:
 retrieve, from a data repository, one or more user identifier attributes for a user identifier associated with the search query; and   apply the machine learning model to the data object attributes and the one or more user identifier attributes to generate the data object relevance scores.   
     
     
         19 . The computer program product of  claim 15 , further comprising instructions that when executed by the one or more computers cause the one or more computers to:
 retrieve, from a data repository, one or more data objects event attributes for one or more data object events with respect to historical visual display elements; and   apply the machine learning model to the data object attributes and the one or more data objects event attributes to generate the data object relevance scores.   
     
     
         20 . The computer program product of  claim 15 , further comprising instructions that when executed by the one or more computers cause the one or more computers to:
 retrieve one or more other data object attributes based at least in part on a query of an attribute graph using at least a portion of the data object attributes; and   apply the machine learning model to the data object attributes and the one or more other data object attributes to generate the data object relevance scores.