Yahoo! Inc.

Baidu Online Network Technology (Beijing) Co., Ltd.

Excalibur IP, LLC

Jiacheng Guo

Siyu You

Quansheng Duan

Jianguo Wang

Li He

Wei Lu

Chen Wang

Lin Liu

Zhen Chen

Sen Liu

Shanyu Wang

Yueran Dang

Cunchao Wei

The present disclosure provides a method and apparatus for detecting significance of promotional information, a device and a computer storage medium. The method comprises: extracting each frame of image in a video segment, taking the image as input of a significance detecting module, and obtaining an output result of the significance detecting model; based on the output result, determining a significance score of promotional information corresponding to said each frame of image; obtaining the significance of promotional information corresponding to the video segment, based on the significance score of promotional information corresponding to each frame of image.

Method and apparatus for detecting significance of promotional information, device and computer storage medium

Techniques for organizing and presenting deals/commercial offers received by users in emails are provided. Emails directed to a user that contain commercial offers for the user are determined. The determined emails are stored in a deal folder for the user. A deal newsletter is generated that at least summarizes commercial offers contained in at least a portion of the emails stored in the deal folder. The deal folder may be displayed to show the user the received deal emails. Furthermore, the deal newsletter may be displayed to the user to summarize the received deals for the user.

Techniques for organizing and presenting deal content

Systems and methods for placing ads in a block on a webpage are disclosed. Generally, two ranking models are trained using a first and second ads data set. The first model predicts a first click probability for each ad in the first ads data and rank the ads based on the eCPM. The second model is trained using the second ads data set comprising a subset of the first ads data set and interaction features related to ad position in the block. The second model predicts a second click probability for each ad in the second ads data set. An overall expected revenue for each arrangement of ads in the second ads data set is then calculated. The computer system selects the arrangement with maximum computed overall expected revenue and places the ads in the block on the webpage according to the selected arrangement.

Systems and methods for list ranking and ads placement using interaction features

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Beijing, China

Jiacheng Guo