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公开(公告)号：US12026180B2
公开(公告)日：2024-07-02
申请号：US17736716
申请日：2022-05-04
申请人： Maplebear Inc.
发明人： Taesik Na , Tejaswi Tenneti , Haixun Wang , Xiao Xiao
IPC分类号： G06F16/28 , G06F16/2457 , G06F16/248 , G06F18/2413
CPC分类号： G06F16/285 , G06F16/24573 , G06F16/24575 , G06F16/248 , G06F18/24147
摘要： 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.
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2.发明公开公开(公告)号：US20230252554A1
公开(公告)日：2023-08-10
申请号：US17669192
申请日：2022-02-10
发明人： Taesik Na , Esther Vasiete
IPC分类号： G06Q30/06 , G06Q10/08 , G06K9/62 , G06F16/9535
CPC分类号： G06Q30/0635 , G06Q10/0875 , G06K9/6215 , G06K9/623 , G06F16/9535
摘要： An online concierge system displays a search interface to users. When displaying suggestions for a query, or displaying results, the online concierge system retrieves candidate suggestions and ranks the candidate suggestions. The online concierge system also obtains an embedding for each candidate suggestion. The online concierge system determines measures of similarity between embeddings for different pairs of candidate suggestion. If a candidate suggestion in a pair has at least a threshold measure of similarity to the other candidate suggestion in the pair, the online concierge system removes one of the candidate suggestions from the pair when displaying candidate suggestions. The online concierge system may remove a candidate suggestion having a lower position in the ranking in a pair of candidate suggestions.
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3.发明公开公开(公告)号：US20240330718A1
公开(公告)日：2024-10-03
申请号：US18625042
申请日：2024-04-02
申请人： Maplebear Inc.
发明人： Li Tan , Tejaswi Tenneti , Shishir Kumar Prasad , Huapu Pan , Taesik Na , Tyler Russell Tate , Joshua Roberts , Haixun Wang
IPC分类号： G06N5/022 , G06F16/901 , G06F40/205 , G06F40/40
CPC分类号： G06N5/022 , G06F16/9024 , G06F40/205 , G06F40/40
摘要： An online system generates a knowledge graph database representing relationships between entities in the online system. The online system generates the knowledge graph database by at least obtaining descriptions for an item. The online system generates one or more prompts to a machine-learned language model, where a prompt includes a request to extract a set of attributes for the item from the description of the item. The online system receives a response generated from executing the machine-learned language model on the prompts. The online system parses the response to extract the set of attributes for the item. For each extracted attribute, the online system generates connections between an item node representing the item and a set of attribute nodes for the extracted set of attributes in the database.
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公开(公告)号：US20240070210A1
公开(公告)日：2024-02-29
申请号：US17899441
申请日：2022-08-30
IPC分类号： G06F16/9532 , G06Q30/06
CPC分类号： G06F16/9532 , G06Q30/0631
摘要： A computer-implemented method for suggesting keywords as a search term of a content item includes receiving, from a content provider, information about the content item in a database of content items. The method further includes generating a set of seed keywords related to the content item, and expanding the set of seed keywords to a plurality of candidate keywords. The plurality of candidate keywords are then scored based, at least in part, on an engagement metric measuring a user engagement with the content item in response to being presented with results from a search query comprising the candidate keyword. A candidate keyword is then selected from the plurality of candidate keywords based on the scoring, and stored relationally to the content item to define an audience for a recommendation about the content item, providing a suggestion to the content provider.
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5.发明公开公开(公告)号：US20230252032A1
公开(公告)日：2023-08-10
申请号：US17666531
申请日：2022-02-07
发明人： Taesik Na , Zhihong Xu , Guanghua Shu , Tejaswi Tenneti , Haixun Wang
IPC分类号： G06F16/2457 , G06F16/242
CPC分类号： G06F16/24578 , G06F16/2438
摘要： An online system maintains various items and maintains values for different attributes of the items, as well as an item embedding for each item. When the online system receives a query for retrieving one or more items, the online system generates an embedding for the query. Based on measures of similarity between the embedding for the query and item embeddings, the online system selects a set of items. The online system identifies a specific attribute of items and generates a whitelist of values for the specific attribute based on measures of similarity between item embeddings for items in the selected set and the embedding for the query. The online system removes items having values for the selected attribute outside of the whitelist of values from the selected set of items to identify items more likely to be relevant to the query.
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公开(公告)号：US20240289861A1
公开(公告)日：2024-08-29
申请号：US18587655
申请日：2024-02-26
申请人： Maplebear Inc.
发明人： Haixun Wang , Tejaswi Tenneti , Taesik Na , Yuanzheng Zhu , Vinesh Reddy Gudla , Lee Cohn
IPC分类号： G06Q30/0601
CPC分类号： G06Q30/0631 , G06Q30/0627 , G06Q30/0635 , G06Q30/0643
摘要： Responsive to an input query from a user, an online system presents a list of recommended items that are related to the input query. The input query may be formulated as a natural language query. The online system performs an inference task in conjunction with the model serving system to generate one or more additional queries that are related to the input query and/or are otherwise related to the recommended items presented in response to the input query. The additional queries may be presented to the user in conjunction with the list of recommended items.
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公开(公告)号：US20240249335A1
公开(公告)日：2024-07-25
申请号：US18159357
申请日：2023-01-25
发明人： Taesik Na , Vinesh Reddy Gudla , Xiao Xiao
IPC分类号： G06Q30/0601 , G06F16/9535 , G06Q30/0201
CPC分类号： G06Q30/0631 , G06F16/9535 , G06Q30/0201
摘要： An online system displays search results in response to a query by receiving a query from a customer. An online system accesses a set of candidate items and computes a relevance score and personalization score for each item. The online system computes the relevance score based on query data and item data and may normalize the relevance score. The online system computes the personalization score based on item data, such as an item embedding, and user data, such as a user embedding. The online system computes a query specificity score and adjusts the personalization score with the query specificity score such that generic queries have high personalization scores and specific queries have low personalization scores. The online system combines the relevance and personalization scores for each candidate item into a ranking score and displays the candidate items to the customer based on their ranking scores.
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公开(公告)号：US20230306023A1
公开(公告)日：2023-09-28
申请号：US17668358
申请日：2022-02-09
发明人： Taesik Na , Yuqing Xie , Tejaswi Tenneti , Haixun Wang
IPC分类号： G06F16/2453 , G06F16/2457 , G06F16/242 , G06F16/28 , G06N20/00 , G06K9/62
CPC分类号： G06F16/24534 , G06F16/2448 , G06F16/24578 , G06F16/283 , G06K9/6257 , G06N20/00
摘要： An online concierge system maintains various items and an item embedding for each item. When the online concierge system receives a query for retrieving one or more items, the online concierge system generates an embedding for the query. The online concierge system trains a machine-learned model to determine a measure of relevance of an embedding for a query to item embeddings by generating training data of examples including queries and items with which users performed a specific interaction. The online concierge system generates a subset of the training data including examples satisfying one or more criteria and further trains the machine-learned model by application to the examples of the subset of the training data and stores parameters resulting from the further training as parameters of the machine-learned model.
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9.发明公开公开(公告)号：US20230273940A1
公开(公告)日：2023-08-31
申请号：US17682187
申请日：2022-02-28
发明人： Guanghua Shu , Taesik Na , Zhihong Xu , Wideet Shende , Manmeet Singh , Tejaswi Tenneti , Reza Sadri
IPC分类号： G06F16/28 , G06F16/22 , G06F16/2455 , G06F11/34
CPC分类号： G06F16/283 , G06F16/2228 , G06F16/24556 , G06F16/285 , G06F11/3409
摘要： An online system maintains item embeddings for items. As a number of items maintained by the online system increases, maintaining a single index of the item embeddings is increasingly difficult. To increase scalability, the online system partitions item embeddings into multiple indices, with each index corresponding to a value of a specific attribute maintained by the online system for items. For example, an online system generates indices that each correspond to a different warehouse offering items. To expedite retrieval of item embeddings, the online system allocates each index to one of a number of shards. When the online system receives a query, the online system determines an embedding for the query and retrieves an index from a shard based on metadata received with the query. Based on distances between the query for the embedding and the item embeddings in the retrieved index, the online system selects one or more items.
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10.发明公开公开(公告)号：US20240311397A1
公开(公告)日：2024-09-19
申请号：US18671761
申请日：2024-05-22
申请人： Maplebear Inc.
发明人： Taesik Na , Tejaswi Tenneti , Haixun Wang , Xiao Xiao
IPC分类号： G06F16/28 , G06F16/2457 , G06F16/248 , G06F18/2413
CPC分类号： G06F16/285 , G06F16/24573 , G06F16/24575 , G06F16/248 , G06F18/24147
摘要： 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.
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