Reinforcement Learning-driven Information Seeking: A Quantum Probabilistic Approach
This work addresses the challenge of understanding user interactions in information retrieval, but it appears incremental as it combines existing reinforcement learning and quantum methods without clear novel breakthroughs.
The paper tackles the problem of modeling information foraging behavior under uncertainty by framing it as a reinforcement learning task, and presents a framework that integrates quantum mechanics to handle this uncertainty, though no concrete numerical results are provided.
Understanding an information forager's actions during interaction is very important for the study of interactive information retrieval. Although information spread in uncertain information space is substantially complex due to the high entanglement of users interacting with information objects~(text, image, etc.). However, an information forager, in general, accompanies a piece of information (information diet) while searching (or foraging) alternative contents, typically subject to decisive uncertainty. Such types of uncertainty are analogous to measurements in quantum mechanics which follow the uncertainty principle. In this paper, we discuss information seeking as a reinforcement learning task. We then present a reinforcement learning-based framework to model forager exploration that treats the information forager as an agent to guide their behaviour. Also, our framework incorporates the inherent uncertainty of the foragers' action using the mathematical formalism of quantum mechanics.