Advertising expenditures have become the major source of revenue for e-commerce platforms. Providing good advertising experiences for advertisers through reducing their costs of trial and error for discovering the optimal advertising strategies is crucial for the long-term prosperity of online advertising. To achieve this goal, the advertising platform needs to identify the advertisers'marketing objectives, and then recommend the corresponding strategies to fulfill this objective . In this work, we first deploy a prototype of strategy recommender system on Taobao display advertising platform, recommending bid prices and targeted users to advertisers . We further augment this prototype system by directly revealing the advertising performance, and then infer the advertisers' marketing objectives through their adoptions of different recommending advertising performance . We use the techniques from context bandit to jointly learn the advertisers' marketing objectives and the recommending strategies. Online evaluations show that the designed advertising strategy recommender system can optimize the advertisers' advertising performance and increase the platform's revenue. Simulation experiments based on Taobao online bidding data show that the designed contextual bandit algorithm can effectively optimize the strategy adoption rate of advertisers.
Advertising expenditures have become the major source of revenue for e-commerce platforms. Providing good advertising experiences for advertisers by reducing their costs of trial and error in discovering the optimal advertising strategies is crucial for the long-term prosperity of online advertising. To achieve this goal, the advertising platform needs to identify the advertiser's optimization objectives, and then recommend the corresponding strategies to fulfill the objectives . In this work, we first deploy a prototype of strategy recommender system on Taobao display advertising platform, which indeed increases the advertisers' performance and the platform's revenue, indicating the effectiveness of strategy recommendation for online advertising . We further augment this prototype system by explicitly learning the advertisers' preferences over various advertising performance indicators and then optimization objectives through their adoptions of different recommending advertising strategies . We use contextual bandit algorithms to efficiently learn the advertisers' preferences and maximize the recommendation adoption, simultaneously. Simulation experiments based on Taobao online bidding data show that the designed algorithms can effectively optimize the strategy adoption rate of advertisers.
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through
by
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for
in
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advertisers'marketing
advertiser's optimization
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this objective
the objectives
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recommending bid prices and targeted users to advertisers
which indeed increases the advertisers' performance and the platform's revenue, indicating the effectiveness of strategy recommendation for online advertising
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directly revealing the advertising performance, and then infer the advertisers' marketing
explicitly learning the advertisers' preferences over various advertising performance indicators and then optimization
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performance
strategies
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the techniques from context bandit to jointly
contextual bandit algorithms to efficiently
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marketing objectives and the recommending strategies. Online evaluations show that the designed advertising strategy recommender system can optimize the advertisers' advertising performance and increase the platform's revenue.
preferences and maximize the recommendation adoption, simultaneously.
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contextual bandit algorithm
algorithms