Context-Based Information Retrieval in Risky Environment
This work addresses a domain-specific issue for users in risky environments where inappropriate exploration could be harmful.
The paper tackles the problem of balancing exploration and exploitation in context-based information retrieval by considering the risk level of the user's situation, introducing the CBIR-R-greedy algorithm to adaptively adjust this balance.
Context-Based Information Retrieval is recently modelled as an exploration/ exploitation trade-off (exr/exp) problem, where the system has to choose between maximizing its expected rewards dealing with its current knowledge (exploitation) and learning more about the unknown user's preferences to improve its knowledge (exploration). This problem has been addressed by the reinforcement learning community but they do not consider the risk level of the current user's situation, where it may be dangerous to explore the non-top-ranked documents the user may not desire in his/her current situation if the risk level is high. We introduce in this paper an algorithm named CBIR-R-greedy that considers the risk level of the user's situation to adaptively balance between exr and exp.