In the evolving landscape of human-centered AI, fostering a synergistic
relationship between humans and AI agents in decision-making processes stands
as a paramount challenge. This work considers a problem setup where an
intelligent agent comprising a neural network-based prediction component and a
deep reinforcement learning component provides advice to a human decision-maker
in complex repeated decision-making environments. Whether the human
decision-maker would follow the agent's advice depends on their beliefs and
trust in the agent and on their understanding of the advice itself. To this
end, we developed an approach named ADESSE to generate explanations about the
adviser agent to improve human trust and decision-making. Computational
experiments on a range of environments with varying model sizes demonstrate the
applicability and scalability of ADESSE. Furthermore, an interactive game-based
user study shows that participants were significantly more satisfied, achieved
a higher reward in the game, and took less time to select an action when
presented with explanations generated by ADESSE. These findings illuminate the
critical role of tailored, human-centered explanations in AI-assisted
decision-making.

在人工智能驱动的决策过程中，提供一种人工智能代理与人类决策者之间协同关系的方法是一项重要挑战。本文介绍了一种名为 ADESSE 的方法，它通过生成解释来提高人类对智能代理的信任和决策能力。实证实验和用户研究结果表明，ADESSE 生成的解释能够显著提高参与者的满意度、游戏奖励并减少选择动作的时间，凸显了人为中心解释在人工智能辅助决策中的重要性。

ADESSE: 在复杂的重复决策环境中的建议解释

ADESSE: Advice Explanations in Complex Repeated Decision-Making  Environments

The integration of multimodal information into sequential recommender systems
has attracted significant attention in recent research. In the initial stages
of multimodal sequential recommendation models, the mainstream paradigm was
ID-dominant recommendations, wherein multimodal information was fused as side
information. However, due to their limitations in terms of transferability and
information intrusion, another paradigm emerged, wherein multimodal features
were employed directly for recommendation, enabling recommendation across
datasets. Nonetheless, it overlooked user ID information, resulting in low
information utilization and high training costs. To this end, we propose an
innovative framework, BivRec, that jointly trains the recommendation tasks in
both ID and multimodal views, leveraging their synergistic relationship to
enhance recommendation performance bidirectionally. To tackle the information
heterogeneity issue, we first construct structured user interest
representations and then learn the synergistic relationship between them.
Specifically, BivRec comprises three modules: Multi-scale Interest Embedding,
comprehensively modeling user interests by expanding user interaction sequences
with multi-scale patching; Intra-View Interest Decomposition, constructing
highly structured interest representations using carefully designed Gaussian
attention and Cluster attention; and Cross-View Interest Learning, learning the
synergistic relationship between the two recommendation views through
coarse-grained overall semantic similarity and fine-grained interest allocation
similarity BiVRec achieves state-of-the-art performance on five datasets and
showcases various practical advantages.

提出了一种创新的框架 BivRec，通过联合训练 ID 和多模态视图的推荐任务，使推荐性能实现双向增强。该框架利用多尺度兴趣嵌入和交叉视图兴趣学习等模块，解决了信息异质性问题，并在五个数据集上取得了最先进的性能。