Microsoft Technology Licensing, LLC

Maplebear Inc.

Prithvishankar Srinivasan

Aman Singhal

Marcelo Medeiros De Barros

Shishir Kumar Prasad

Donald Frank Brinkman, Jr

Max Artemov

Shih-Ting Lin

Min Xie

Saurav Manchanda

Riddhima Sejpal

Pranathi Reddy Tupakula

Marcelo M Debarros

Deep Narayan Dubey

Luis Manrique

Embodiments relate to utilizing a language model to automatically generate a novel recipe with refined content, which can be offered to a user of an online system. The online system generates a first prompt for input into a large language model (LLM), the first prompt including a plurality of task requests for generating initial content of a recipe. The online system requests the LLM to generate, based on the first prompt input into the LLM, the initial content of the recipe. The online system generates a second prompt for input into the LLM, the second prompt including the initial content of the recipe and contextual information about the recipe. The online system requests the LLM to generate, based on the second prompt input into the LLM, refined content of the recipe. The online system stores the recipe with the refined content in a database of the online system.

Using language model to generate recipe with refined content

A computer system uses clustering and a large language model (LLM) to normalize attribute tuples for items stored in a database of an online system. The online system collects attribute tuples, each attribute tuple comprising an attribute type and an attribute value for an item. The online system initially clusters the attribute tuples into a first plurality of clusters. The online system generates prompts for input into the LLM, each prompt including a subset of attribute tuples grouped into a respective cluster of the first plurality. Based on the prompts, the LLM generates a second plurality of clusters, each cluster including one or more attribute tuples that have a common attribute type and a common attribute value. The online system maps each attribute tuple to a respective normalized attribute tuple associated with each cluster. The online system rewrites each attribute tuple in the database to a corresponding normalized attribute tuple.

Using unsupervised clustering and language model to normalize attribute tuples of items in a database

The present disclosure relates to systems and methods for automatically associating additional content with sports games. The systems and methods obtain game schedule data for a plurality of sports games, and for each scheduled game, the systems and methods select articles published within a timeframe of the game date of the scheduled game. The systems and methods identify sports articles associated with the sports game based on entities and event phrases extracted from the sports articles matching the schedule game data. The systems and methods classify the sports articles into a plurality of clusters and use the clusters to associate the sports articles to the game schedule data and store the associated sports articles with the game schedule data.

Associating articles to corresponding events

Examples of the present disclosure describe systems and methods that provide a pipeline to generate personalized queries that are associated with and based on a user's interests determined from a user's past searches, on an Internet search engine, and/or the content the user viewed from the past searches. The suggested queries can be shown in a user interface component associated with the user interface of the search engine and before the user enters anything, such as a new Internet search. This pre-population of searches associated with a user's interests gives an opportunity to the user to try these queries without manually entering in a search string.

Methods and systems for personalized, zero-input suggestions based on semi-supervised activity clusters

A method of selectively providing different types of search engine results to different searchers is provided. A browsing history for each of a plurality of unique identities is logged. A unique identity is associated with a rich segment experience responsive to the browsing history for the unique identity satisfying correlation criteria of the rich segment experience. The rich segment experience is configured to present curated segment-specific information with other search results on a search result web page. Responsive to receiving a search query from the unique identity previously associated with the rich segment experience, the rich segment experience is presented with other search results on the search result web page. Responsive to receiving the search query from a different unique identity not previously associated with the rich segment experience, other search results are presented without the rich segment experience on the search result web page.

Selective presentation of rich experiences in search

Aspects of the present disclosure are directed to providing a rich content experience based on information received from unstructured content. A plurality of information items may be obtained from a plurality of data source, where each information item includes unstructured content. The plurality of information items may be provided to a trained machine learning model, where the model is trained with training data that includes information items and corresponding labeled entities for a plurality of historical events. In examples, a formatted request may be received, where the formatted request is associated with one or more labeled entities associated with the trained machine learning model. The trained machine learning model may identify multiple entities from the unstructured content based on the formatted request associated with the one or more labeled entities. In examples, each identified entity of the multiple identified entities is stored as structured content responsive to the formatted request.

Generating structured data for rich experiences from unstructured data streams

The present concepts relate to an improved personalized search engine that can generate personalized rankings of search results in view of individual user's personal preferences and interests. Information about a segment of online content is collected. Certain activities by a user are tracked, including search queries submitted by the user, search results clicked on by the user, and/or web pages browsed by the user. From these activities, the user's preferences relating the segment are inferred using the collected segment information. When the user conducts a search directed to the segment, certain search results that the user is more likely to be interested in, based on the user's preferences, are ranked higher to generate the personalized rankings.

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Seattle, WA, United States of America

Prithvishankar Srinivasan