Methods and apparatuses are provided for use in monitoring product placement within a shopping facility. Some embodiments provide an apparatus configured to determine product placement conditions within a shopping facility, comprising: a transceiver configured to wirelessly receive communications; a product monitoring control circuit coupled with the transceiver; a memory coupled with the control circuit and storing computer instructions that when executed by the control circuit cause the control circuit to: obtain a composite three-dimensional (3D) scan mapping corresponding to at least a select area of the shopping facility and based on a series of 3D scan data; evaluate the 3D scan mapping to identify multiple product depth distances; and identify, from the evaluation of the 3D scan mapping, when one or more of the multiple product depth distances is greater than a predefined depth distance threshold from the reference offset distance of the product support structure.

A real-time conversation is monitored between a user and an intelligent virtual assistant (IVA). A visualization may be generated and displayed to the user on the user computing device based on one or more topics identified in the conversation. The conversation between the user and the IVA may continue and is continued to be monitored. The visualization can be updated as the conversation continues, e.g., based on further topics being identified.

This disclosure provides systems, methods and apparatuses for recommending items to users of a recommendation system. In some implementations, the recommendation system determines a plurality of contribution values based on interactions between a plurality of users and a plurality of items. Each of the plurality of contribution values represents a confidence level that a respective user prefers a respective item. The recommendation system further determines a capacity of each of the plurality of items. The capacity of each item represents a maximum number of users to which the item can be recommended. The recommendation system recommends one or more items of the plurality of items to each of the plurality of users based at least in part on the plurality of contribution values and the capacities of the plurality of items.

A method for shopping from a social media site using a direct buy link comprises electronically storing a plurality of account profiles and receiving a data message electronically transmitted via a communication network. The method may query a social media database for an account profile associated with the consumer identifier and the PIN. The method may display a plurality of direct buy links associated with a plurality of products for purchase, wherein at least one of the direct buy links is a modified direct buy link. The method may receive a data message electronically transmitted via the communication network and display the modified direct buy link comprising the personal data corresponding to the product, a purchase data, and payment preference information. The method may transmit confirmation of payment and the modified direct buy link associated with the purchased product to the consumer device.

A purchasing agent application operates on a mobile device to facilitate a user's purchase of a product featured on a web page of a third party merchant. The user can select an option in a web browser to share the web page URL with the application. The application communicates with a server to streamline the purchase process based on previously entered user identity, payment and shipping information. The application and/or server use machine learning models to automate the completion of any one or more form fields and/or link selections that the user would otherwise be required complete on the third party merchant's web site to execute the purchase. None of the user, the application or the server need have any prior relationship with the third party merchant. The machine learning models can be trained, for example, using supervised learning, unsupervised learning and/or reinforcement learning techniques.

A system and method for facilitating delivery of one or more products is disclosed. The method includes receiving a request from a purchaser to purchase one or more products from a desired store and receiving a mode of delivery of the one or more products from the purchaser. The method further includes determining the personal shopper for delivering the one or more products from the desired store to the purchaser based on the received request, received mode of delivery and predefined information by using a product delivery based AI model and obtaining an approval from the personal shopper for the received request of the purchaser. The method includes generating an order schedule and a dynamic navigation map for the personal shopper. Further, the method includes outputting the order schedule and the dynamic navigation map on a graphical user interface of one or more purchaser devices and personal shopper device.

A system and method may be provided for more efficient onboarding of entities to a marketplace. An existing entity in the marketplace may agree to become an agent of the owner of the marketplace. A request may be received from a new entity wishing to join the marketplace. The system and method may assign the existing entity to onboard the new entity. A digital image may be received of the existing entity and new entity and optional documentation of the new entity's credentials. The system may digitally link the first seller and second seller so that the first seller may receive a portion of the transaction values collected by the second seller going forward.

Aspects of the subject disclosure may include, for example, storing, in a personal inventory database, first identification information associated with a first item purchased by a first user at a first website of a first merchant, the first identification information being obtained electronically from one or more first servers that provide the first website; storing, in the personal inventory database, second identification information associated with a second item purchased by the first user at a second website of a second merchant, the second merchant being a different merchant than the first merchant, the second website being a different website than the first website, the second identification information being obtained electronically from one or more second servers that provide the second website, and each of the one or more second servers being a different server than each of the one or more first servers; receiving first web browsing data based upon monitoring first web browsing of the first user, the monitoring of the first web browsing being performed by another device; determining, based upon the first web browsing data and based upon the first identification information, a first suggested item for purchase by the first user; and transmitting to the another device that performs the monitoring an identification of the first suggested item to facilitate a presentation of the identification of the first suggested item to the first user. Other embodiments are disclosed.

A hypergraph is constructed based on historical shopping cart data. A node of the hypergraph corresponds to a shopping basket, and a hyperedge of the hypergraph corresponds to a unique product, the hyperedge connecting all nodes of the hypergraph representing baskets containing the unique product. A hypergraph partition algorithm identifies a cluster of shopping baskets represented in the hypergraph and determined to be similar to a given basket. Based on the cluster of shopping baskets a dual-level return prediction is performed. The dual-level return prediction includes predicting whether the given basket will be returned, and based on predicting that the given basket will be returned, predicting a probability that a product in the given basket will be returned. Based on predicting that the given basket will be returned, an ameliorative action is performed to reduce the probability.

An online system provides options for selection by a user. The online system receives a query entered on a client device. The online system queries an item database to retrieve a set of items related to the query and assigns each item to a product category in a predefined taxonomy that maps items to product categories. The online system inputs each item into a prediction model trained to predict a probability that an item is available at a warehouse location. The online system determines that a first product category has low availability based on predicted probabilities for items in the first product category. Responsive to determining that a first product category has low availability, the online system generates a generic item for the first product category and sends a list of items including the generic item to the client device for display responsive to the query.