Abstract: The disclosed systems and methods provide a system for dynamically serving personalized content for presentation across computing devices of different guests in an online retail environment. A server system can: retrieve, from a data repository, a subset of predefined content stories having a predetermined activation status and guest purchase data for at least a portion of guests in the online retail environment, the guest purchase data indicating products each guest purchased, determine product embeddings for each story in the subset, each embedding indicating a product linked to the story, determine, based on applying a guest story similarity (GSS) model to the guest purchase data for each guest, a similarity score between the guest purchase data and the embeddings that indicates a likelihood the guest would purchase products embedded in the story, and serve a content story having a similarity score that satisfies one or more guest personalization criteria.

Abstract: A system and method for efficiently serving personalized offer content across digital platforms through a unified content architecture are provided. The system includes a data repository storing single-instance content elements and a server system that dynamically serves offer stories while performing real-time eligibility verification. The server system maintains a unified content pool, enabling automatic adaptation of content across different channels while reducing computational resource usage through consolidated storage. The system performs real-time eligibility checks through integration with loyalty services, dynamically collapses offer slots when users are ineligible, and automatically updates offer visibility based on eligibility changes. The system enables querying and serving of content while supporting real-time updates to display manner as user behavior and eligibility changes.

Abstract: The disclosed systems and methods provide a system for dynamically serving personalized content for presentation across computing devices of different guests in an online retail environment. A server system can: retrieve, from a data repository, a subset of predefined content stories having a predetermined activation status and guest purchase data for at least a portion of guests in the online retail environment, the guest purchase data indicating products each guest purchased, determine product embeddings for each story in the subset, each embedding indicating a product linked to the story, determine, based on applying a guest story similarity (GSS) model to the guest purchase data for each guest, a similarity score between the guest purchase data and the embeddings that indicates a likelihood the guest would purchase products embedded in the story, and serve a content story having a similarity score that satisfies one or more guest personalization criteria.

Abstract: The disclosed systems and methods provide a system for dynamically serving personalized content for presentation across computing devices of different guests in an online retail environment. A server system can: retrieve, from a data repository, a subset of predefined content stories having a predetermined activation status and guest purchase data for at least a portion of guests in the online retail environment, the guest purchase data indicating products each guest purchased, determine product embeddings for each story in the subset, each embedding indicating a product linked to the story, determine, based on applying a guest story similarity (GSS) model to the guest purchase data for each guest, a similarity score between the guest purchase data and the embeddings that indicates a likelihood the guest would purchase products embedded in the story, and serve a content story having a similarity score that satisfies one or more guest personalization criteria.