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Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds
Most e-commerce product feeds provide blended results of advertised prod...
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Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
In E-commerce, advertising is essential for merchants to reach their tar...
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Technical Report: Distribution Temporal Logic: Combining Correctness with Quality of Estimation
We present a new temporal logic called Distribution Temporal Logic (DTL)...
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A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction
For e-commerce platforms such as Taobao and Amazon, advertisers play an ...
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Planning and Acting under Uncertainty: A New Model for Spoken Dialogue Systems
Uncertainty plays a central role in spoken dialogue systems. Some stocha...
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Deep Reinforcement Learning for Image Hashing
Deep hashing methods have received much attention recently, which achiev...
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Sequential Multi-Class Labeling in Crowdsourcing
We consider a crowdsourcing platform where workers' responses to questio...
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Learning to Infer User Hidden States for Online Sequential Advertising
To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important. The lack of interpretability in existing deep reinforcement learning methods makes it not easy to understand, diagnose and further optimize the strategy. In this paper, we propose our Deep Intents Sequential Advertising (DISA) method to address these issues. The key part of interpretability is to understand a consumer's purchase intent which is, however, unobservable (called hidden states). In this paper, we model this intention as a latent variable and formulate the problem as a Partially Observable Markov Decision Process (POMDP) where the underlying intents are inferred based on the observable behaviors. Large-scale industrial offline and online experiments demonstrate our method's superior performance over several baselines. The inferred hidden states are analyzed, and the results prove the rationality of our inference.
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