Online Query-Based Data Pricing with Time-Discounting Valuations

Online data marketplaces emerge in diverse data-driven applications, where dynamically arriving consumers pur-chase the data at posted prices. The data value decays over time in many tasks, such as machine learning predictions and realtime systems. Existing query pricing methods do not consider the time-discounting data value. In this paper, we study the query feature-based data pricing problem with unknown time-discounting data valuation. We propose an effective online data pricing mechanism Pride to maximize the cumulative sales revenue. It leverages the powerful property of the ellipsoid method to efficiently solve online optimization via exploration and exploitation. Based on Thompson sampling, we present a novel non-stationary MAB algorithm Biased-TS to determine a suitable discount factor and attain the dynamic posted price. It is theoretically proved that, the regret upper bound order of Pride is dominated by the discretization error
$O(\frac{T}{k})$
, where
$K$
and
$T$
are the numbers of discount candidates and total trading rounds, respectively. Biased-TS gets a sub-linear regret upper bound
$O(K^{3}\sqrt{T\ln T}+K\exp\{4\sqrt{\ln T}\})$
. Extensive experiments using both synthetic and real datasets demonstrate that Pride yields around 90% of the optimal cumulative revenue, and it substantially outperforms the state-of-the-art methods.