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Register a new userDynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue
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Motivated by the observation that overexposure to unwanted marketing activities leads to customer dissatisfaction, we consider a setting where a platform offers a sequence of messages to its users and is penalized when users abandon the platform due to marketing fatigue. We propose a novel sequential choice model to capture multiple interactions taking place between the platform and its user: Upon receiving a message, a user decides on one of the three actions: accept the message, skip and receive the next message, or abandon the platform. Based on user feedback, the platform dynamically learns users abandonment distribution and their valuations of messages to determine the length of the sequence and the order of the messages, while maximizing the cumulative payoff over a horizon of length T. We refer to this online learning task as the sequential choice bandit problem. For the offline combinatorial optimization problem, we show that an efficient polynomial-time algorithm exists. For the online problem, we propose an algorithm that balances exploration and exploitation, and characterize its regret bound. Lastly, we demonstrate how to extend the model with user contexts to incorporate personalization.
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Motivated by the phenomenon that companies introduce new products to keep abreast with customers rapidly changing tastes, we consider a novel online learning setting where a profit-maximizing seller needs to learn customers preferences through offering recommendations, which may contain existing products and new products that are launched in the middle of a selling period. We propose a sequential multinomial logit (SMNL) model to characterize customers behavior when product recommendations are presented in tiers. For the offline version with known customers preferences, we propose a polynomial-time algorithm and characterize the properties of the optimal tiered product recommendation. For the online problem, we propose a learning algorithm and quantify its regret bound. Moreover, we extend the setting to incorporate a constraint which ensures every new product is learned to a given accuracy. Our results demonstrate the tier structure can be used to mitigate the risks associated with learning new products.
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The Thresholding Bandit Problem (TBP) aims to find the set of arms with mean rewards greater than a given threshold. We consider a new setting of TBP, where in addition to pulling arms, one can also emph{duel} two arms and get the arm with a greater mean. In our motivating application from crowdsourcing, dueling two arms can be more cost-effective and time-efficient than direct pulls. We refer to this problem as TBP with Dueling Choices (TBP-DC). This paper provides an algorithm called Rank-Search (RS) for solving TBP-DC by alternating between ranking and binary search. We prove theoretical guarantees for RS, and also give lower bounds to show the optimality of it. Experiments show that RS outperforms previous baseline algorithms that only use pulls or duels.
We propose SLTD (`Sequential Learning-to-Defer) a framework for learning-to-defer pre-emptively to an expert in sequential decision-making settings. SLTD measures the likelihood of improving value of deferring now versus later based on the underlying uncertainty in dynamics. In particular, we focus on the non-stationarity in the dynamics to accurately learn the deferral policy. We demonstrate our pre-emptive deferral can identify regions where the current policy has a low probability of improving outcomes. SLTD outperforms existing non-sequential learning-to-defer baselines, whilst reducing overall uncertainty on multiple synthetic and real-world simulators with non-stationary dynamics. We further derive and decompose the propagated (long-term) uncertainty for interpretation by the domain expert to provide an indication of when the models performance is reliable.
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