公开(公告)号：US20240354556A1

公开(公告)日：2024-10-24

申请号：US18640231

申请日：2024-04-19

Applicant: Maplebear Inc.

Inventor： Yueyang Rao ,  Brian Lin ,  Angadh Singh ,  Sharath Rao Karikurve ,  Guanghua Shu

IPC: G06N3/0455 ,  G06Q30/0601

CPC classification number: G06N3/0455 ,  G06Q30/0631

Abstract: An online system generates session-based recommendations for a user accessing an application of the online system. The online system receives, from one or more client devices, a sequence of actions performed by a user during a session of an application of an online system. The online system generates a sequence of tokens from the sequence of actions by tokenizing an action to a token representing a respective item identifier. The online system applies a transformer-based machine-learned model to the sequence of tokens to generate predictions for a set of items. The online system selects a subset of items based on the generated predictions for the set of items. The online system generates one or more recommendations to the user from the selected subset of items and displays the recommendations to the user.

公开(公告)号：US20220335489A1

公开(公告)日：2022-10-20

申请号：US17232621

申请日：2021-04-16

Applicant: Maplebear, Inc.(dba Instacart)

Inventor： Sharath Rao Karikurve ,  Angadh Singh

IPC: G06Q30/06 ,  G06N20/00 ,  G06F17/16

Abstract: An online concierge system maintains information about items offered for purchase and users of the online concierge system. Based on prior purchases of items by users, the online concierge system trains a model to determine a likelihood of a user purchasing an item based on an embedding for the object and embedding for the user. The online concierge system identifies a collection of items and generates an embedding for the collection. The collection may be a cluster of items determined from similarities between embeddings of items. Alternatively, the collection may be a group of items having a common category. The online concierge system includes one or more collections of items along with individual items when recommending items for the users, so the trained model is applied to embeddings of the individual items and to embeddings of the one or more collections to generate recommendations for a user.

公开(公告)号：US20250078133A1

公开(公告)日：2025-03-06

申请号：US18240157

申请日：2023-08-30

Applicant: Maplebear Inc.

Inventor： Brian Lin ,  Angadh Singh ,  Sharath Rao Karikurve

IPC: G06Q30/0601 ,  G06Q30/0201

Abstract: Content items are presented to users based on sensitivity scores indicating sensitivity levels of users to relevance of content items to queries. A system receives a query from a target user, retrieves a set of search results responsive to the query, and retrieves a set of content items, each of which has a relevance score to the query. The system applies a machine learning model to user data of the target user to output a sensitivity score, indicating a sensitivity level of the target user to relevance of content item to the query. The system then selects one or more content items based on the sensitivity score and the relevance scores of the content items, incorporates the selected content items into the search results, and sends the search results with the selected content items for display to the target user.

公开(公告)号：US20250061350A1

公开(公告)日：2025-02-20

申请号：US18233828

申请日：2023-08-14

Applicant: Maplebear Inc.

Inventor： Ganesh Krishnan ,  Sharath Rao Karikurve ,  Angadh Singh ,  Changyao Chen ,  Tilman Drerup

IPC: G06N5/022 ,  G06Q10/087

Abstract: An online system trains a churn prediction model to attribute a churn event to one or more causal events. The churn prediction model receives customer features and online system features as inputs. Various causal events that occur affect one or more online system features. To avoid biasing the churn prediction model using input features that are related to possible causal events, the online system determines customer features and online system features based on customer interactions occurring in different time intervals. The customer features are determined from interactions in a time interval that is earlier than a time interval from which interactions are used to determine online system features. Such time segmenting decorrelates the features input to the model from the events, reducing potential bias from the causal events on the churn prediction model.

公开(公告)号：US11282126B1

公开(公告)日：2022-03-22

申请号：US16450935

申请日：2019-06-24

Applicant: Maplebear, Inc.

Inventor： Daniel Hsiao ,  Angadh Singh ,  Jason Scott ,  Daniel Shilov

IPC: G06Q30/06

Abstract: An online concierge system accesses an order history database, which describes historical orders for a plurality of users. Each historical order comprises a list of items selected by a user from a plurality of items. The online concierge system determines user streaks, each user streak based on a number of consecutive orders placed by a user that include a particular item. The online concierge system calculates an overall streak distribution based on the user streaks. For each item of at least a subset of the plurality of items, the online concierge system calculates a streak statistic based on a corresponding subset of user streaks and the overall streak distribution. The online concierge system selects a set of staples items for a user based on the streak statistics for the set of staple items and generates a display for the user with a selectable list of the set of staple items.