Utilizing machine learning models for data-driven customer segmentation

A device, comprising:

• one or more memories;
• and
• one or more processors, coupled to the one or more memories, configured to:
• train, jointly, a long short-term memory based encoder-decoder model and a knowledge graph embedding model;
• receive purchase data identifying purchases by users of user devices and identifying non-temporal data associated with the users;
• preprocess the purchase data to generate sequences of multivariate and multimodal symbols;
• process the sequences of multivariate and multimodal symbols, with the long short-term memory based encoder-decoder model, to generate sequence embeddings;
• process the non-temporal data associated with the users, with a knowledge graph, to determine knowledge graph embeddings capturing the non-temporal data associated with the users;
• process the sequence embeddings and the knowledge graph embeddings, with the knowledge graph embedding model, to generate fine-tuned user embeddings that capture temporal and non-temporal features;
• process the fine-tuned embeddings, with a clustering model, to determine clusters of the users in relation to products or services purchased by the users;
• and
• perform one or more actions based on the clusters of the users.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive purchase data identifying purchases by users of user devices and identifying non-temporal data associated with the users;
• preprocess the purchase data to generate temporal data associated with the users;
• process the temporal data associated with the users, with a long short-term memory based encoder-decoder model, to generate sequence embeddings;
• process the non-temporal data associated with the users, with a knowledge graph, to determine knowledge graph embeddings capturing the non-temporal data associated with the users;
• process the sequence embeddings and the knowledge graph embeddings, with a knowledge graph embedding model, to generate fine-tuned user embeddings that capture temporal and non-temporal features;
• process the fine-tuned user embeddings, with a clustering model, to determine clusters of the users in relation to products or services purchased by the users;
• and
• perform one or more actions based on the clusters of the users.