Oath Inc.

Excalibur Ip, LLC

Yahoo Assets LLC

Pengyuan Wang

Yi Chang

Jean-Marc Langlois

Ayman Farahat

Jian Yang

Jing Jin

Suleyman Cetintas

Dawei Yin

Puneet Mohan Sangal

Hua Ouyang

The present teaching relates to ranking search content. In one example, a plurality of documents is received to be ranked with respect to a query. Features are extracted from the query and the plurality of documents. The plurality of documents is ranked based on a ranking model and the extracted features. The ranking model is derived to remove one or more documents from the plurality of documents that are less relevant to the query and order remaining documents based on their relevance to the query. The ordered remaining documents are provided as a search result with respect to the query.

Method and system for ranking search content

Systems, devices, and methods are disclosed for determining the quality of traffic received from different web page publishers, and setting a pricing structure for the different traffic based on the determined quality of traffic. Accurately determining the quality of traffic and/or clicks from different publishers allows the network system described herein to offer a fair marketplace with just return on investments (ROI) for advertisers, and offer a robust and accurate traffic quality based pricing model for publishers. Internet based technology, and in particular deep learning techniques available through a neural network, are utilized to determine the pricing structure based on click and/or web page traffic quality measurements generated through the deep learning techniques.

System and method for traffic quality based pricing via deep neural language models

Systems and methods for tracking brand reputation and market share are provided. The system includes one or more devices having a processor and a non-transitory storage medium accessible to the hardware processor. The device is programmed to obtain an awareness index at a plurality of levels at least partially based on the brand data. The device is programmed to obtain a favorability index as a ratio of user numbers based on the brand data. The device is programmed to obtain a branding index by combining the awareness index and the favorability index. The device is programmed to obtain an affinity score for a group of users at least partially based on the brand data and recommend the group of users based on the affinity score to increase the branding index.

Systems and methods for tracking brand reputation and market share

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Sunnyvale, CA, United States of America

Pengyuan Wang