EATR: Emotion-aware temporal reinforcement learning for serendipitous multi-device recommendation ecosystems

In this work, we introduce Emotion-Aware Temporal Reinforcement Learning (EATR) to optimize serendipitous recommendations on multi-device environments. The existing literature is based on optimal timing by employing reinforcement learning, but it does not always consider the affective conditions of the users and cross-device temporal responses that influence the perceived serendipity. We synthesize cross-device temporal feedback loops with real-time affective computing signals into one policy learning scheme in this work. Through a hierarchical reinforcement learning, we can align the timing of recommendations to devices, including smartphone, wearable, and smart TVs. Experiments on a dataset (artificially created, augmented with emotion signals) and realistic data of an interaction between devices demonstrate that EATR makes serendipity, engagement, and long-term reward highly effective compared to the current state of the art. This study provides an exploratory contribution toward integrating affective computing, temporal decision-making, and intra-/inter-device recommendation mechanisms within a unified reinforcement learning framework.