Abstract: An online concierge system receives information from a warehouse including locations of items within the warehouse. When a shopper selects an order for fulfillment from the warehouse, the online concierge system sorts the items for the shopper to minimize the time spent in the warehouse using the received information. When the online concierge system does not receive a location of an item within the warehouse, the online concierge system obtains a taxonomy for the warehouse including multiple levels, with each level having a different level of specificity. The online concierge system determines a higher level in the taxonomy for the item and identifies other items offered by the warehouse having the determined category. The online concierge system infers a location of the item within the warehouse used for sorting items of the order from locations of the other items within the warehouse and times when shoppers retrieved the other items.

Abstract: An online shopping concierge system identifies a set of delivery orders and a set of delivery agents associated with a location. The system allocates the orders among the agents, each agent being allocated at least one order. The system obtains agent progress data describing travel progress of the agents to the location, and order preparation progress data describing progress of preparing the orders for delivery. The system periodically updates the allocation of the orders among the agents based on the agent progress data and the order preparation progress data. This involves re-allocating at least one order to a different delivery agent. When a first agent arrives at the location, the system assigns to the first agent the orders allocated to the first agent. The system then removes the first agent from the set of available delivery agents, and removes the assigned delivery orders from the set of delivery orders.

Abstract: When analyzing operation of an online system, such as an online concierge system, that receive queries from users and return results in response to those queries, queries received by the online system may include different numbers of terms. The variations in numbers of terms included in different queries makes it difficult to efficiently group queries for analysis, resulting in inefficient analysis of individual queries when analyzing different queries based on their structure. To more efficiently analyze different queries, the online system groups queries based on a number of terms in the query. In some embodiments, queries are grouped based on a logarithmic scale of the number of terms in the query, while in other embodiments queries are grouped based on a square root of the number of terms in the query.

Abstract: In an online concierge system, a shopper retrieves items specified in an order by a customer from a retail location. The online concierge system optimizes order fulfillment by selecting a retail location for an order that is most time-efficient and that is most likely to have each of the item in the order available. Hence, the online concierge system may select a less convenient retail location that is more likely to have each item being ordered available. To predict whether a retail location incompletely fulfill the order if selected to fulfill the order, the online concierge system trains a machine learning model based on prior orders fulfilled by the retail location, a shopper retrieving items in the order, items in the order, and other features.

Abstract: Techniques for facilitating the autonomous presentation of a self-driving vehicle are provided. In one example, a method can include a system operatively coupled to a processor, where the system: determines a feature of a self-driving vehicle based on information regarding an entity in a pending transaction; determines a task to be performed by the self-driving vehicle based on the feature; and generates an instruction for the self-driving vehicle to perform the task.

Abstract: An online concierge system performs asynchronous automated correction handling of incorrectly sorted items using point-of-sale data. The online concierge system receives orders from customer client devices and determines a batched order based on the received orders. The online concierge system sends the batched order to a shopper client device for fulfillment. The online concierge system receives transaction data associated with the batched order from a third party system. The online concierge system determines whether a sorting error occurred based on the transaction data and the batched order. In response to determining that a sorting error occurred, the online concierge system sends an instruction to correct the sorting error to the shopper client device.

Abstract: A barcode decoding system decodes item identifiers from images of barcodes. The barcode decoding system receives an image of a barcode and rotates the image to a pre-determined orientation. The barcode decoding system also may segment the barcode image to emphasize the portions of the image that correspond to the barcode. The barcode decoding system generates a binary sequence representation of the item identifier encoded in the barcode by applying a barcode classifier model to the barcode image, and decodes the item identifier from the barcode based on the binary sequence representation.

Abstract: An online concierge system uses a model to predict a user's interaction with an item, based on a user embedding for the user and an item embedding for the item. For the model to account for more recent interactions by users with items without retraining the model, the online concierge system generates updated item embeddings and updated user embeddings that account for the recent interactions by users with items. The online concierge system compares performance of the model using the updated item embeddings and the updated user embeddings relative to performance of the model using the existing item embeddings and user embeddings. If the performance of the model decreases, the online concierge system adjusts the updated user embeddings and the updated item embeddings based on the change in performance of the model. The adjusted updated user embeddings and adjusted updated item embeddings are stored for use by the model.

Abstract: An online concierge system allows users to order items from a warehouse, which may have multiple warehouse locations. The online concierge system provides a user interface to users for ordering the items, with the user interface providing an indication of whether an item is predicted to be available at the warehouse at different times. To predict availability of an item model at different times, the online concierge system selects data from historical information about availability of items at one or more warehouses based on temporal, geospatial, and socioeconomic information about observations of historical availability of items at warehouses. The online concierge system accounts for distances between observations and a time and geographic location in a feature space to select observations for predicting item availability at the time and the geographic location.

Abstract: An online concierge system modifies generic item descriptions included in a recipe displayed to a user based on the user's preferences. The online concierge system generates a replacement graph identifying a replacement generic item description for a generic item description, one or more preferences causing replacement of the generic item description with the replacement generic item description, and a replacement quantity of the replacement generic item description. To customize a recipe for the user, the online concierge system selects replacement generic item descriptions for one or more generic item descriptions in the recipe satisfying one or more stored preferences for the user based on the replacement graph.

Abstract: An online concierge system delivers items from retailers to customers. The online concierge predicts a range of times during which an order may be fulfilled for presentation to a user. The online concierge system uses a trained maximum time prediction model to determine a maximum time for order fulfillment based on an order. A trained minimum time prediction model determines a minimum time for order fulfillment from the order and the maximum time. The minimum time may account for one or more rules (e.g., a percentage of orders fulfilled before the minimum time, a desired rate of selection of a range including the minimum time). A range bounded by the maximum time and the minimum time is transmitted to a customer to enable the customer to select a time interval for order fulfillment.

Abstract: An online system leverages stored interactions with items made by users after the online system received queries to determine display of items satisfying the query. For example, the online system trains a model to predict a likelihood of a user performing an interaction with an item displayed after a query was received. As different items receive different amounts of interaction from users, limited historical interaction with certain items may limit accuracy of the model. The online system generates embeddings for previously received queries and uses measures of similarity between embeddings for queries to generate clusters of queries. Previous interactions with queries in a cluster are combined, with the combined data being used for determining display of items in response to a query.

Abstract: An augmented reality application executing on a client device receives video data captured by a camera of the device, in which the video data includes a display area of the device. The application detects a set of items within the display area based on the video data, wherein the set of items is included among an inventory of a warehouse associated with a retailer, and accesses a set of attributes of each item. The application retrieves profile information including a set of preferences associated with a customer of the retailer, matches one or more of the set of preferences with one or more attributes of each item, and generates an augmented reality element based on the matches. The augmented reality element is then displayed, such that it is overlaid onto a portion of the display area based on a location within the display area at which the items are detected.

Abstract: Systems and methods for a collaborative offer portal is provided. A proposed offer is received from a manufacturer, including an offer structure and a number of consumers they wish to target. Transaction logs of a retailer are accessed to determine an audience for the offer by calculating a return on investment (ROI) for the customer base using the retailer's records given the offer type. The consumers are then grouped by their ROI distribution, and the ROI for the deal is calculated based upon the offer size in light of this distribution. From the offer ROI a discount percentage to be paid by the retailer versus the merchant can be created. The retailer may then choose to accept the offer for deployment.

Abstract: An online concierge system trains a machine learning conversion model that predicts a probability of receiving an order from a user when the user accesses the online concierge system. The conversion model predicts the probability of receiving the order based on a set of input features that include price and availability information. For each access to the online concierge system, the online concierge system applies the conversion model to a current price and availability and to an optimal price availability. The online concierge system generates a metric as the difference between the two predicted probabilities of receiving an order.

Abstract: An online concierge system allows users to order items from a warehouse having multiple physical locations, allowing a user to order items at any given warehouse location. To select a warehouse location for a warehouse selected by a user, the online concierge system identifies a set of items that the user has a threshold likelihood of purchasing from prior orders by the user. For each of a set of warehouse locations, the online concierge system applies a machine-learned item availability model to each item of the identified set. From the availabilities of items of the set at each warehouse location of the set, the online concierge system selects a warehouse location. The online concierge system identifies an inventory of items from the selected warehouse location to the user for inclusion in an order.

Abstract: An online concierge system maintains a taxonomy associating one or more specific items offered by a warehouse with a generic item description. When the online concierge system receives a generic item description from a user for inclusion in an order, the online concierge system uses the taxonomy to select a set of items associated with the generic item description. Based on probabilities of the user purchasing various items of the set, the online concierge system selects an item of the set for inclusion in the order For example, the online concierge system selects an item of the set for which the user has a maximum probability of being purchased. Subsequently, the online concierge system displays an interface for the user that is prepopulated with information identifying the selected item of the set.

Abstract: An online concierge shopping system identifies candidate items to a user for inclusion in an order based on prior user inclusion of items in orders and items currently included in the order. From a multi-dimensional tensor generated from cooccurrences of items in orders from various users, the online concierge system generates item embeddings and user embeddings in a common latent space by decomposing the multi-dimensional tensor. From items included in an order, the online concierge system generates an order embedding from item embeddings of the items included in the order. Scores for candidate items are determined based on similarity of item embeddings for the candidate items to the order embedding. Candidate items are selected based on their scores, with the selected candidate items identified to the user.