Practical Lessons from Predicting Clicks on Ads at Facebook

Xinran He, Stuart Bowers, Joaquin QuiƱonero Candela, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich
Proceedings of 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining - ADKDD'14

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abstract Online advertising allows advertisers to only bid and pay for measurable user responses, such as clicks on ads. As a consequence, click prediction systems are central to most online advertising systems. With over 750 million daily active users and over 1 million active advertisers, predicting clicks on Facebook ads is a challenging machine learning task. on 2/29/2020, 11:17:55 PM

excerpt In most online advertising platforms the allocation of ads is dynamic, tailored to user interests based on their observed feedback. 1 on 2/29/2020, 11:18:13 PM

excerpt In sponsored search advertising, the user query is used to retrieve candidate ads, which explicitly or implicitly are matched to the query. At Facebook, ads are not associated with a query, but instead specify demographic and interest targeting. As a consequence of this, the volume of ads that are eligible to be displayed when a user visits Facebook can be larger than for sponsored search. 1 on 2/29/2020, 11:18:44 PM

excerpt The efficiency of an ads auction depends on the accuracy and calibration of click prediction. The click prediction system needs to be robust and adaptive, and capable of learning from massive volumes of data. 1 on 2/29/2020, 11:20:31 PM

Deep Neural Networks for YouTube Recommendations cites Practical Lessons from Predicting Clicks on Ads at Facebook on 2/29/2020, 11:17:23 PM