Advertisers play an important role in e-commerce platforms, whose advertising expenditures are the main source of revenue for e-commerce platforms. Therefore, providing advertisers with a better advertising experience by reducing their cost of trial and error during ad real-time bidding is crucial to the long-term revenue of e-commerce platforms . To achieve this goal, the advertising platform needs to understand the advertisers' unique marketing demands and actively recommend personalized and optimal advertising strategies for them . In this work, we first deploy a prototype  recommender system on Taobao display advertising platform for constant bid and crowd optimization. Then, we propose a novel recommender system for dynamic bidding strategy recommendation, which models the advertiser' s strategy recommendation problem as a contextual bandit problem . We use a neural network as the agent to predict the  advertisers' demands based on their profile and historical adoption behaviors. Based on the estimated demand, we apply simulated bidding to derive the optimal bidding strategy for recommendation and interact with the advertiser by displaying the possible advertising performance. To solve the exploration/exploitation dilemma, we use Dropout to represent the uncertainty of the network, which approximately equals to conduct Thompson sampling for efficient strategy exploration. Online evaluations show that the  system can optimize the advertisers' advertising performance , and advertisers are willing to open the system, select and adopt the suggestions, which further increases the platform's revenue income . Simulation experiments based on Alibaba online bidding data prove that the agent can effectively optimize the  adoption rate of advertisers , and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model .
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 .
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Advertisers play an important role in e-commerce platforms, whose advertising expenditures are the main
Advertising expenditures have become the major
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Therefore, providing advertisers with a better advertising experience by reducing their cost
Providing good advertising experiences for advertisers through reducing their costs
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during ad real-time bidding is crucial to
for discovering the optimal advertising strategies is crucial for
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revenue of e-commerce platforms
prosperity of online advertising
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understand
identify
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unique marketing demands and actively recommend personalized and optimal advertising strategies for them
marketing objectives, and then recommend the corresponding strategies to fulfill this objective
A
of strategy
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for constant bid and crowd optimization. Then, we propose a novel recommender system for dynamic bidding strategy recommendation, which models the advertiser' s strategy recommendation problem as a contextual bandit problem
, 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
D
a neural network as the agent to predict
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techniques from context bandit to jointly learn the
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demands based on their profile and historical adoption behaviors. Based on the estimated demand, we apply simulated bidding to derive the optimal bidding strategy for recommendation and interact with the advertiser by displaying the possible advertising performance. To solve the exploration/exploitation dilemma, we use Dropout to represent the uncertainty of the network, which approximately equals to conduct Thompson sampling for efficient strategy exploration.
marketing objectives and the recommending strategies.
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designed advertising strategy recommender
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, and advertisers are willing to open the system, select and adopt the suggestions, which further increases the
and increase the
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income
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Alibaba
Taobao
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prove that the agent
show that the designed contextual bandit algorithm
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strategy
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, and Thompson sampling can better balance exploration and exploitation to further optimize the performance of the model