Methods and apparatus for determining attribute affinities for users

Abstract

In some examples, a system to may be configured to, for at least a first user of the plurality of users, implement a first set of operations that generate, for each of a first set of item types, attribute value data. Additionally, the system may implement a second set of operations that generate, for each of a second set of item types identified in catalogue data, clique data. Moreover, the system may, for the at least first user, implement a third set of operations that generate preference dependency data . Further, the system may, for the at least first user, based on the preference dependency data, the clique data, the attribute value data, generate, for each item type of a set of item types, output data including an affinity value for each item type of the first set of item types.

What is claimed is: 
     
         1 . A system comprising:
 a database storing catalogue data and, for each user of a plurality of users of an online platform, transaction data, engagement data and search query data;   at least one processor; and   a memory resource storing instructions, that when executed by the at least one processor, causes the at least one processor to:
 for at least a first user of the plurality of users:
 based on the transaction data of the at least first user, implement a first set of operations that generate, for each of a first set of item types, attribute value data characterizing an attribute value for each attribute feature of one or more items associated with each of the first set of item types; 
 based on the catalogue data, implement a second set of operations that generate, for each of a second set of item types identified in the catalogue data, clique data characterizing one or more cliques, each of the one or more cliques identifying a subset of attribute features of a set of attribute features that are mutually exclusive; 
 based at least in part on the transaction data, engagement data and search query data of at least the first user, implement a third set of operations that generate dependency data characterizing one or more dependencies between each attribute feature of a set of predetermined attribute features; and 
 based on the dependency data, the clique data, the attribute value data, generate, for each item type of a set of item types, output data including an affinity value for each item type of the first set of item types. 
 
   
     
     
         2 . The system of  claim 1 , wherein the first set of operations includes:
 based on the transaction data of at least the first user, identifying the first set of item types;   based on the transaction data, generating, for each item type of the first set of item types, attribute feature data, the attribute feature data characterizing a set of attribute features;   based on the transaction data, the engagement data and set of user attribute features, generate attribute value data that indicates, for each of the set of attribute features, the attribute value.   
     
     
         3 . The system of  claim 1 , wherein the affinity value of each item type of the set of item types indicates a likelihood of an occurrence of a purchase event between the at least first user and one or more items of the corresponding item type. 
     
     
         4 . The system of  claim 1 , wherein each attribute feature is associated with a preference of a preference set, the set of preferences including at least one of (i) type preferences for various products, (ii) price sensitivity at a product/product-type level, (iii), brand sensitivity and preferences, (iv) restriction preferences, (v) restricted foods preferences, (vi) dietary methods preferences, (vii) dietary needs preferences, (viii) allergens preferences, (viv) container types preferences, and (x) quantity preferences. 
     
     
         5 . The system of  claim 1 , wherein the attribute value data characterizes a likelihood of an occurrence of a purchase event between the at least first user and a particular item of a particular item type with the corresponding attribute feature. 
     
     
         6 . The system of  claim 1 , wherein the second set of operations includes:
 based on the catalogue data, generating, for each item type identified from the catalogue data, a graph clustering; and   generating, for each item type identified from the catalogue data, complementary graph clusters based on a corresponding graph clustering.   
     
     
         7 . The system of  claim 1 , wherein the clique data is further based on search query data. 
     
     
         8 . The system of  claim 1 , wherein the generating, for each item type of the first set of item types, the output data includes applying a Naïve Bayes model to attribute value data of the at least first user. 
     
     
         9 . The system of  claim 8 , wherein the generating, for each item type of the first set of item types, the output data includes applying the clique data to the Naïve Bayes model. 
     
     
         10 . The system of  claim 9 , wherein the generating, for each item type of the first set of item types, the output data includes updating the Naïve Bayes model with the dependency data. 
     
     
         11 . A computer-implemented method comprising:
 for at least a first user of a plurality of users:
 based on transaction data of at least the first user, implementing a first set of operations that generate, for each of a first set of item types, attribute value data characterizing an attribute value for each attribute feature of one or more items associated with each of the first set of item types; 
 based on catalogue data, implementing a second set of operations that generate, for each of a second set of item types identified in the catalogue data, clique data characterizing one or more cliques, each of the one or more cliques identifying a subset of attribute features of a set of attribute features that are mutually exclusive; 
 based at least in part on the transaction data, engagement data and search query data of at least the first user, implementing a third set of operations that generate dependency data characterizing one or more dependencies between each attribute feature of a set of predetermined attribute features; and 
 based on the dependency data, the clique data, the attribute value data, generating, for each item type of a set of item types, output data including an affinity value for each item type of the first set of item types. 
   
     
     
         12 . The computer-implemented method of  claim 11 , wherein the first set of operations includes:
 based on the transaction data of at least the first user, identifying the first set of item types;   based on the transaction data, generating, for each item type of the first set of item types, attribute feature data, the attribute feature data characterizing a set of attribute features;   based on the transaction data, the engagement data and set of user attribute features, generate attribute value data that indicates, for each of the set of attribute features, the attribute value.   
     
     
         13 . The computer-implemented method of  claim 11 , wherein the affinity value of each item type of the set of item types indicates a likelihood of an occurrence of a purchase event between the at least first user and one or more items of the corresponding item type. 
     
     
         14 . The computer-implemented method of  claim 11 , wherein each attribute feature is associated with a preference of a preference set, the set of preferences including at least one of (i) type preferences for various products, (ii) price Sensitivity at a product/product-type level, (iii), brand Sensitivity and Preferences, (iv) Restriction preferences, (v) Restricted Foods Preferences, (vi) Dietary Methods Preferences, (vii) Dietary Needs Preferences, (viii) Allergens Preferences, (viv) Container Types Preferences, and (x) Quantity Preferences. 
     
     
         15 . The computer-implemented method of  claim 11 , wherein the attribute value data characterizes a likelihood of an occurrence of a purchase event between the at least first user and a particular item of a particular item type with the corresponding attribute feature. 
     
     
         16 . The computer-implemented method of  claim 11 , wherein the second set of operations includes:
 based on the catalogue data, generating, for each item type identified from the catalogue data, a graph clustering; and   generating, for each item type identified from the catalogue data, complementary graph clusters based on a corresponding graph clustering.   
     
     
         17 . The computer-implemented method of  claim 11 , wherein the generating, for each item type of the first set of item types, the output data includes applying a Naïve Bayes model to attribute value data of the at least first user. 
     
     
         18 . The computer-implemented method of  claim 17 , wherein the generating, for each item type of the first set of item types, the output data includes applying the clique data to the Naïve Bayes model. 
     
     
         19 . The computer-implemented method of  claim 18 , wherein the generating, for each item type of the first set of item types, the output data includes updating the Naïve Bayes model with the preference dependency data. 
     
     
         20 . A non-transitory computer readable medium storing instructions, that when executed by at least one processor, causes a system to:
 for at least a first user of the plurality of users:
 based on transaction data of at least the first user, implement a first set of operations that generate, for each of a first set of item types, attribute value data characterizing an attribute value for each attribute feature of one or more items associated with each of the first set of item types; 
 based on the catalogue data, implement a second set of operations that generate, for each of a second set of item types identified in the catalogue data, clique data characterizing one or more cliques, each of the one or more cliques identifying a subset of attribute features of a set of attribute features that are mutually exclusive; 
 based at least in part on the transaction data, engagement data and search query data of at least the first user, implement a third set of operations that generate dependency data characterizing one or more dependencies between each attribute feature of the set of predetermined attribute features; and 
 based on the dependency data, the clique data, the attribute value data, generate, for each item type of a set of item types, output data including an affinity value for each item type of the first set of item types.