Microsoft Technology Licensing, LLC

Meta Platforms, Inc.

Sushma Nagesh Bannur

Omar Rogelio Alonso

Martin Hentschel

Chinmay Deepak Karande

Shyamsundar Rajaram

Robert Oliver Burns Zeldin

Leon R Cho

Rami Mahdi

An online system calculates bids for content items to display to users based on the value of a product described in the content item and the likelihood of a viewing user purchasing the product. The online system identifies an impression opportunity for an ad request and computes an expected value of the conversion and a likelihood of the conversion. The online system computes a bid amount based on the expected conversion value and the likelihood of the conversion. Bids based on the value of the conversion allow a third party system offering the product to optimize for the value of each conversion instead of the conversion rate.

Calculating bids for content items based on value of a product associated with the content item

Systems and methods are presented for generating a transition graph from check-in data for determining probabilistic transitions of a user from an origin (location) to a destination. Check-in data for a plurality of users is obtained. The check-in data is organized and analyzed to determine a set of transition tuples for each of the plurality of users. These transition tuples are aggregated and used to generate a transition graph comprising a plurality of nodes and edges. Each node corresponds to a location and each edge indicates a transition from an origin node to a destination node and is associated with a likelihood of a user transitioning from the origin node to the destination node.

Generating probabilistic transition data

One or more techniques and/or systems are provided for metadata tag evaluation. For example, a metadata tag, associated with content, may be identified (e.g., a hashtag #ML may be used to tag a social network post). A set of characters, within the content, may be evaluated utilizing a probability matrix and the content to identify an expanded metadata tag (e.g., an expanded hashtag 'machine learning'). Descriptive content, such as websites, articles, social network posts, and/or other content associated with the expanded metadata tag, may be retrieved. A description for the metadata tag may be generated based upon the descriptive content (e.g., a definition for machine learning). In this way, the description, related metadata tags, and/or supplemental content may be provided to users having an interest in learning about the metadata tag.

Metadata tag description generation

One or more techniques and/or systems are provided for augmenting search results with a user defined suggestion and/or for providing a user defined suggestion. For example, a venue entity may be associated with search results and/or user information (e.g., a set of search results may correspond to the Eiffel Tower). User defined data associated with the venue entity may be evaluated to generate a user defined suggestion associated with the venue entity. For example, user generated content about the Eiffel Tower, such as tips, advice, opinions, and/or other information specified by social network users, may be evaluated to determine a user defined suggestion 'get tower tickets early, they sell out fast'. The user defined suggestion may be provided to a user (e.g., visiting the venue entity) and/or used to augment the set of search results. In this way, practical information, derived from opinions shared by other users, may be provided.

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Cuperino, CA, United States of America

Sushma Nagesh Bannur