Abstract: An item recommendation control platform is disclosed. The item recommendation control platform presents an administrative user one or more user interfaces at which recommendation rules may be defined. Each of the recommendation rules may be associated with one or more nodes within a retail website, such as an item detail page or an item category page. The recommendation rules may be selected, defined, and prioritized, such that one or more item recommendation rules may be reflected within a given item recommendation presented on a retail website. The item recommendation rules can include one or more filters, the filters controlling which items may be recommended to a given user based, for example, on item availability or shipping availability.

Abstract: A multi-modal machine learning model is disclosed that may be implemented in a recommender system. The model may generate a multi-modal embedding based on a user query, one or more user-selected items, and a conversation history. The one or more user-selected items may include text data and visual data. In some embodiments, the recommender system may use the multi-modal embeddings to recommend one or more items of a plurality of items. In some embodiments, the multi-modal model may be integrated with other systems.

Abstract: An in-store system for empty shelf detection substantially reduces computational resources required to determine where out-of-stock conditions are present. Such systems can prioritize analysis of product displays having different levels of turnover or importance, and can use on-site resources to make quick and accurate determinations of out-of-stock conditions that impact customer satisfaction.

Abstract: An item recommendation control platform is disclosed. The item recommendation control platform presents an administrative user one or more user interfaces at which recommendation rules may be defined. Each of the recommendation rules may be associated with one or more nodes within a retail website, such as an item detail page or an item category page. The recommendation rules may be selected, defined, and prioritized, such that one or more item recommendation rules may be reflected within a given item recommendation presented on a retail website. The item recommendation rules can include one or more filters, the filters controlling which items may be recommended to a given user based, for example, on item availability or shipping availability.