R-UCB: a Contextual Bandit Algorithm for Risk-Aware Recommender Systems

TL;DR

This paper introduces R-UCB, a novel algorithm for mobile context-aware recommender systems that adaptively balances exploration and exploitation by considering the user's risk level, enhancing recommendation safety and effectiveness.

Contribution

The paper proposes R-UCB, a new contextual bandit algorithm that incorporates risk awareness to improve recommendation decisions in mobile systems.

Findings

R-UCB effectively balances exploration and exploitation based on risk levels.

Experimental results show improved user satisfaction and safety.

The approach outperforms traditional bandit algorithms in risky scenarios.

Abstract

Mobile Context-Aware Recommender Systems can be naturally 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 recommend items the user may not desire in her current situation if the risk level is high. We introduce in this paper an algorithm named R-UCB that considers the risk level of the user's situation to adaptively balance between exr and exp. The detailed analysis of the experimental results reveals several important discoveries in the exr/exp behaviour.