Abstract: An online concierge system receives, from a client device associated with a user, a request to access a user interface including a listing of sources associated with the system, in which each source is associated with a catalog of items. The system retrieves user data describing interactions by the user with items available at the sources and accesses and applies a machine-learning model to predict a user engagement score for each item-source pair associated with the sources based on the user data, in which the score indicates a likelihood of an interaction by the user with an item available at a source. The system selects a set of item-source pairs based on the scores and generates the user interface including the listing and a selectable option to add an item associated with each selected pair to a shopping list. The system then sends the user interface to the client device.

Abstract: A cart management system generates an error priority assignment for smart cart systems based on device error predictions for those smart cart systems. An error priority assignment is an assignment of the relative priority of servicing or providing maintenance to a set of smart cart systems. To generate the error priority assignment, the cart management system applies an error detection model to cart data received from the set of smart cart systems. The cart data has measurements captured by sensors coupled to the smart cart systems, and the error detection model uses the cart data to generate device error predictions. Each of these predictions represents a likelihood that a smart cart system will experience a device error within some time period. The cart management system uses the device error predictions to generate the error priority assignment and selects which smart cart system to service based on the error priority assignment.

Abstract: An online system trains a ceiling prediction model to determine a user's ceiling for one or more item categories. The user's ceiling for an item category is a maximum amount of an item within the item category the user is likely to include in an order. Based on previously fulfilled orders for the user, information describing a current order from the user, and contextual information about the order, the ceiling prediction model determines the user's ceiling for an item category. The online system leverages the user's ceiling for an item category to refine content about different items that is selected for presentation to a user. For example, the online system determines whether the order includes a quantity of items from an item category that equals the user's ceiling for the item category when determining which items to present to the user.

Abstract: A trained model is used to generate market adjustment recommendations for a retailer associated with an online system. Upon displaying an item to a user of the online system for replacing an originally requested item and collecting user's engagement data in relation to the replacement item, the online system accesses a market adjustment model that is trained to generate a score for the user indicative of an affinity of the user in relation to the replacement item and generate one or more market adjustment recommendations for the retailer. The online system applies the market adjustment model to generate, based on the engagement data, behavioral information of the user and/or contextual information associated with the user, the score for the user and the one or more market adjustment recommendations for the retailer. The online system provides the one or more market adjustment recommendations to a computing system associated with the retailer.

Abstract: An online system leverages a cart issue prediction model trained as a machine-learning model to identify predicted issues with the operation of a smart cart based on sensor data captured by sensors on the smart cart and/or feedback from a user of the smart cart. The machine-learning model is trained on historical data related to the operation of a fleet of smart carts. In response to identifying any cart issues, the online system can trigger a remedial workflow to remedy the predicted issues. The online system may transmit command signals to the smart cart to calibrate the sensors, may transmit remedial tasks to a client device of the user for prompting the user to aid in remedying the predicted issues, or may schedule a service appointment with a technician, including an issue report indicating the predicted issues.

Abstract: An online system uses a trained machine-learning model to predict a perception of a user about an expiration date of an item. Upon receiving an item signal including information about the expiration date, the online system applies the machine-learning model to the item signal, information about the user, and information about the item to generate a perception score indicative of a likelihood that the user will perceive the expiration date as unacceptable. Based on the perception score and the information about the item, the online system identifies a second item for replacing the item, the second item having a second expiration date that is later than the expiration date. The online system generates, using information about the item and the second item, a user interface signal that causes a user interface to display a notification about the expiration date and a recommendation for replacing the item with the second item.

Abstract: An online concierge system uses a findability machine-learning model to predict the findability of items within a physical area. The findability model is a machine-learning model that is trained to compute findability scores, which are scores that represent the ease or difficulty of finding items within a physical area. The findability model computes findability scores for items based on an item map describing the locations of items within a physical area. The findability model is trained based on data describing pickers that collect items to service orders for the online concierge system. The online concierge system aggregates this information across a set of pickers to generate training examples to train the findability model. These training examples include item data for an item, an item map data describing an item map for the physical area, and a label that indicates a findability score for that item/item map pair.

Abstract: An online system leverages a cart issue prediction model trained as a machine-learning model to identify predicted issues with the operation of a smart cart based on sensor data captured by sensors on the smart cart and/or feedback from a user of the smart cart. The machine-learning model is trained on historical data related to the operation of a fleet of smart carts. In response to identifying any cart issues, the online system can trigger a remedial workflow to remedy the predicted issues. The online system may transmit command signals to the smart cart to calibrate the sensors, may transmit remedial tasks to a client device of the user for prompting the user to aid in remedying the predicted issues, or may schedule a service appointment with a technician, including an issue report indicating the predicted issues.

Abstract: An online concierge system selectively replaces default static item displays with dynamic item displays to represent items. The dynamic item displays encourage a viewing user of the online concierge system to purchase the items and may be selected based on item or user preferences or characteristics. The online concierge system applies a machine learning model to determine display scores describing the expected benefit of dynamic item displays and bandwidth scores describing resource usage of dynamic item displays. The online concierge system selectively replaces default static item displays with dynamic item displays based on the display and bandwidth scores so as to maximize benefit while ensuring that performance of the online concierge system is not negatively impacted by the resource usage.

Abstract: A client device, or an online system communicating with the device, receives video data depicting a field of view of a display area of the device and applies machine-learning algorithms to the video data to detect objects, including portions of a body of a user of the device, within the field of view and to determine a series of body poses. The device/system uses machine-learning models to predict an action performed by the user based on the series of poses and to predict a recipe being prepared based on the objects and a predicted series of actions performed by the user. The device/system selects a suggestion associated with preparing the recipe based on candidate suggestions associated with preparing the recipe, the objects, or the predicted series of actions, and generates an augmented reality element describing the suggestion. The augmented reality element is displayed in the display area of the device.

Abstract: An online system trains a multimodal machine-learning model to predict a rate of using an item that can be ordered at the online system by a user. The machine-learning model is trained by using a plurality of training examples, where each training example includes training images associated with a respective training user that are related to a respective item from the collection of items, and data related to conversion of the respective item by the respective training user. Upon receiving images of user's physical spaces that store items, the online system applies the trained machine-learning model to the images to output a rate of using a specific item by the user. Based on the predicted rate, the online system generates a user interface signal causing a device associated with the user to display a user interface with a user interface element for use by the user to restock the item.

Abstract: An online system uses a trained machine-learning model to detect errors in catalog data based on interactions of users of the online system with physical carts. Upon receiving an interaction signal indicating an interaction by the user with a device in a location of a source or an action signal indicating an action in the location of the source, the online system applies the trained model to the interaction signal and/or the action signal to generate an error score for an item that indicates a likelihood of an error in relation to the item. Responsive to the error score being above a threshold score, the online system generates an error checking signal for confirming that the error is present. Responsive to the confirmation of the error, the online system generates a user interface that alerts about the error and requests an action to correct the error.

Abstract: The present disclosure is directed to determining purchase suggestions for an online shopping concierge platform. In particular, the methods and systems of the present disclosure may receive, from a computing device associated with a customer of an online shopping concierge platform, data indicating one or more interactions of the customer with the online shopping concierge platform; determine, based at least in part on one or more machine learning (ML) models and the data indicating the interaction(s), a likelihood that the customer will purchase a particular item if presented, at a specific time, with a suggestion to purchase the particular item; and generate and communicate data describing a graphical user interface (GUI) comprising at least a portion of a listing of one or more purchase suggestions including the suggestion to purchase the particular item.

Abstract: A system receives real-time sensor data from sensors of a smart cart. The system identifies a triggering event based on the sensor data. The system obtains a template for the triggering event, wherein the template comprises instructions for generating suggestions for the user to augment smart cart operation. The system may obtain other contextual information, e.g., order data, user data, source data about a source location, etc. The system generates a prompt by modifying the template to include the sensor data or the contextual information. The system causes execution of the prompt by a language model, which outputs a response based on the prompt. The system generates augmented content including the suggestions for the user by parsing the response. The augmented content may be multimodal, combining multiple forms of data. The system transmits the augmented content for presentation to the user to augment operation of the smart cart.

Abstract: A trained model of an online system is used to generate action recommendations by predicting future demands. The online system gathers in-store data by receiving, from a device of a picker and/or a computing system of an in-store physical receptacle, data with information about an inventory of an item. The online system estimates, based on conversion data for the item, a level of inventory for the item. The trained model is then applied to predict, based on the in-store data and the estimated level of inventory, a demand prediction score indicative of a future demand for the item. The online system generates, based on the estimated level of inventory and the demand prediction score, a depletion metric indicative of a time period until the inventory of the item is depleted. Based on the depletion metric, the online system triggers an action in relation to the inventory of the item.

Abstract: An online system uses a trained model for intelligent handling of unclaimed online pickup orders. After identifying that an order placed by a user of the online system is unclaimed at a location of a source, the online system obtains, from a device of a picker associated with the online system and/or a device associated with the source, signals with information about each item in each bundle of the unclaimed order. The online system applies the trained model to identify, based on the obtained signals, a preferred method for disposal of each bundle. Based on the identified preferred method for disposal of each bundle, the online system generates a disposal decision signal and communicates the disposal decision signal to the device associated with the source that prompts personnel at the location of the source to dispose each bundle of the unclaimed order using the identified preferred disposal method.

Abstract: An online system receives a request from a client device associated with a user to place an order for pickup from a source location during a timeframe and identifies candidate remedial actions associated with the order based on the timeframe and a current time. The system retrieves user data for the user and accesses a machine-learning model. For each candidate remedial action, the system applies the model to predict, based on the user data and order data for the order, a likelihood the user will pick up the order if the candidate remedial action is taken and computes an associated value based on the likelihood. The system selects a remedial action from the candidate remedial actions based on the values, generates, based on the selected remedial action, a message associated with the order that includes a set of selectable options, and sends the message to the client device.

Abstract: A device interfaced with an online system detects, via a physical sensor, item removal and generates a user interface with an alternative option for conversion. Upon receiving a signal from the device indicating the item removal, the online system selects a set of candidate items for replacement of the removed item, wherein each candidate item has a conversion value that is less than a conversion value of the removed item. The online system applies a trained machine-learning model to generate a conversion score for each candidate item that indicates a likelihood of conversion by the user of each candidate item. The online system selects, based on the conversion score for each candidate item, a replacement item from the set of candidate items, and generates a user interface signal that causes a user interface of the device to prompt the user to convert the replacement item.

Abstract: An online concierge system dynamically determines types of shopping events. The types may be used in various ways to increase efficiency of an item pipeline. The system may monitor interactions of a customer with an ordering interface on a customer client device associated with the customer. The monitoring may be during a shopping event that is categorized by a type, wherein the type describes a purpose of the shopping event. Responsive to a monitored interaction being an interaction from a set of trigger interactions, the system may determine a type of shopping event by applying the monitored interaction and content of a shopping cart of the ordering interface to a type prediction model. The system may assign an updated type to be the determined type, and perform an action based in part on the updated type.

Abstract: A trained model is used to predict a scheduled delivery for a self-picked order. Responsive to receiving an indication from a device associated with a user of an online system that the device is either within a defined vicinity from a location of a retailer or physically present at the location of the retailer, the online system applies a user targeting computer model trained to generate, based on user data and ordering data, a score for the user indicative of a likelihood of the user accepting an offer for the scheduled delivery of the order. Responsive to the score being greater than a threshold score, the online system generates a list of service options for the scheduled delivery of the order and displays the list of service options at a user interface of the device prompting the user to select a service option for the scheduled delivery of the order.