Facebook, Inc.

Yahoo! Inc.

Raghavendra Rao Donamukkala

Shyamsundar Rajaram

Chinmay Deepak Karande

Darshan Vishwanath Kantak

Robert Oliver Burns Zeldin

Zheng Chen

Nimish Rameshbhai Shah

Leon R Cho

Toby Jonas F Roessingh

The present disclosure is directed toward systems and methods for optimizing view-through conversion rates. For example, systems and methods described herein train and utilize a machine learning model that predicts whether providing a digital impression to a particular networking system user will result in a conversion. Systems and methods described herein identify view-through conversions by generating a vector associated with the provision of a digital impression to a networking system user and receiving third-party conversion information during an attribution window. The systems and methods described herein then utilize the vector and conversion information to train the machine learning model to predict future conversions.

Optimizing conversion rates for impressions provided in a networking system

The present disclosure is directed toward systems, methods, and non-transitory computer readable media that use multi-point optimization for delivery of digital content by way of a digital content distribution platform. In particular, one or more embodiments described herein receive a content item from a content provider to be displayed to users of the platform. The embodiments optimize delivery to obtain a first target event and determine metrics that delivery of the content item is expected to satisfy. If the actual metrics of delivery fail to satisfy the expected metrics, delivery is re-optimized to obtain either the first target event or a second target event.

Utilizing multi-point optimization to improve digital content distribution

A method for processing structured search queries received at an internet search includes receiving at the internet search site a structured search query containing a user's search intent and other pertinent information about the user and the site. The structured search query is constructed as a conforming structured search string. The method further includes parsing the structured search query based on a grammar published for affiliates or partners to construct the structured search query. Still further, the method includes extracting individual breadcrumbs from the structured search query that were used to build the structured search query. This includes decoding the position and meaning of each respective breadcrumb in the structured search query. The method further includes forming match tokens from the identified individual breadcrumbs and combinations of breadcrumbs. Still further the method includes matching listings stored in a search listing database with the formed match token strings, forming a search result list, and ranking elements of the search result list according to strength of match tokens to the original user search intent.

Structured match in a directory sponsored search system

Growing community of inventors

San Jose, CA, United States of America

Raghavendra Rao Donamukkala