Providing dynamically customized rankings of game items

A computer-implemented method comprising:

• obtaining user data for a plurality of users of an online gaming platform, the user data including user profile data and behavioral parameters;
• applying a clustering algorithm to the user data to obtain a plurality of clusters, wherein each cluster includes one or more of the plurality of users, and wherein no user is in more than one cluster;
• and
• training a plurality of machine learning models, wherein each model corresponds to a respective cluster of the plurality of clusters and wherein the plurality of machine learning models have a same model structure, wherein the training includes, for each model:
• providing an input feature vector that includes a plurality of feature values for each item of a plurality of items;
• obtaining an output feature vector wherein the output feature vector includes a modified value for each of the plurality of feature values for each item;
• generating a rank for each item based on the output feature vector;
• and
• adjusting at least one model parameter of the model based on comparison of the generated rank with an known rank for each item.

A system comprising:

• a memory with instructions stored thereon;
• and
• a processing device, coupled to the memory, the processing device operable to access the memory and execute the instructions, wherein the instructions cause the processing device to perform operations comprising:
• obtaining user data for a plurality of users of an online gaming platform, the user data including user profile data and behavioral parameters;
• applying a clustering algorithm to the user data to obtain a plurality of clusters, wherein each cluster includes one or more of the plurality of users, and wherein no user is in more than one cluster;
• and
• training a plurality of machine learning models, wherein each model corresponds to a respective cluster of the plurality of clusters and wherein the plurality of machine learning models have a same model structure, wherein the training includes, for each model:
• providing an input feature vector that includes a plurality of feature values for each item of a plurality of items;
• obtaining an output feature vector wherein the output feature vector includes a modified value for each of the plurality of feature values for each item;
• generating a rank for each item based on the output feature vectors;
• and
• adjusting at least one model parameter of the model based on comparison of the generated rank with an known rank for each item.

A non-transitory computer-readable medium with instructions stored thereon that, responsive to execution by a processing device, causes the processing device to perform operations comprising:

• receiving, from a user, a request for a list of items available on an online gaming platform;
• identifying a particular trained machine learning model from a plurality of trained machine learning models based at least in part on user data associated with the user;
• providing an input feature vector to the particular trained machine learning model, wherein the input feature vector includes a plurality of feature values for each item of a plurality of items;
• obtaining an output feature vector from the particular trained machine learning model, wherein the output feature vector includes a modified feature value for each of the plurality of feature values for each item;
• assigning a rank for each of the plurality of items based on the output feature vector;
• generating the list of items, wherein items in the list of items are ordered based on respective ranks;
• and
• providing a user interface to the user that includes the list of items.