Yahoo Assets LLC

Oath Inc.

Verizon Media Inc.

Parikshit Shah

Akshay Soni

Troy Chevalier

Kapil Raj Thadani

Sreekanth Ramakrishnan

Aaron Nagao

Zhi Qu

Rohit Chandra

Ming M Yang

Adwait Ratnaparkhi

Ben M Shahshahani

Rao Shen

Sachidanand Alle

Yue Ning

A method is provided, including: storing comments generated in response to a content item served over a network; analyzing the comments to determine features associated with each of the comments; using a scoring model to score each comment based on the comment's corresponding features; receiving a request to serve a subset of the comments; responsive to the request, selecting a ranking of the comments that is one permutation from possible rankings of the comments, wherein selecting the ranking is in accordance with a probability distribution of the possible rankings that is based on the scores of the comments; serving comments identified by the selected ranking over the network to a client device; determining a dwell time on the served comments; applying the dwell time to update the scoring model.

Ranking user comments on media using reinforcement learning optimizing for session dwell time

Systems and methods are disclosed for discovering precursors associated with a current user interaction event. One method comprises receiving a selection of a new item by a user and determining a plurality of similarities between the new item selected by the user and a plurality of historical items, the plurality of historical items being associated with prior user activity. Then a plurality of importance weights associated with the plurality of historical items are determined. Based on the plurality of similarities and the plurality of importance weights, at least one enhanced importance matrix is generated. Further, prior interactions of the user with the plurality of historical items are determined. Based on the enhanced importance matrix and the prior interactions of the user with the plurality of historical items, precursors for the new item selected by the user are identified and provided to a display.

Analytical precursor mining for personalized recommendation

A system and method enable exploration of cold ads in an online information system. Cold ads are ads that are new to the system and do not yet have reliable click through rate (CTR) estimates or click probabilities. The method and system selectively boost the click probabilities of cold ads to increase their likelihood of participating in and winning an auction so that the cold ad get more impressions, but without adversely affecting revenue in a production environment when cold ads are introduced.

Exploration for search advertising

Growing community of inventors

Emerald Hills, CA, United States of America

Parikshit Shah