Food is a key pleasure of traveling, but travelers face a trade-off between exploring curious new local food and choosing comfortable, familiar options. This creates demand for personalized recommendation systems that balance these competing factors. To the best of our knowledge, conventional recommendation methods cannot provide recommendations that offer both curiosity and comfort for food unknown to the user at a travel destination. In this study, we propose new quantitative methods for estimating comfort and curiosity: Kernel Density Scoring (KDS) and Mahalanobis Distance Scoring (MDS). KDS probabilistically estimates food history distribution using kernel density estimation, while MDS uses Mahalanobis distances between foods. These methods score food based on how their representation vectors fit the estimated distributions. We also propose a ranking method measuring the balance between comfort and curiosity based on taste and ingredients. This balance is defined as curiosity (return) gained per unit of comfort (risk) in choosing a food. For evaluation the proposed method, we newly collected a dataset containing user surveys on Japanese food and assessments of foreign food regarding comfort and curiosity. Comparing our methods against the existing method, the Wilcoxon signed-rank test showed that when estimating comfort from taste and curiosity from ingredients, the MDS-based method outperformed the Baseline, while the KDS-based method showed no significant differences. When estimating curiosity from taste and comfort from ingredients, both methods outperformed the Baseline. The MDS-based method consistently outperformed KDS in ROC-AUC values.

翻译：美食是旅行的重要乐趣之一，但旅行者面临着探索新奇地方美食与选择舒适熟悉选项之间的权衡。这催生了对能够平衡这两种竞争因素的个性化推荐系统的需求。据我们所知，传统推荐方法无法为旅行目的地的用户未知食物提供兼具好奇心与舒适度的推荐。在本研究中，我们提出了估算舒适度与好奇心的新定量方法：核密度评分法（Kernel Density Scoring, KDS）与马氏距离评分法（Mahalanobis Distance Scoring, MDS）。KDS通过核密度估计概率性地评估饮食历史分布，而MDS则利用食物间的马氏距离。这些方法基于食物表征向量与估计分布的匹配程度进行评分。我们还提出了一种基于口味和成分衡量舒适度与好奇心平衡的排序方法，该平衡定义为选择食物时每单位舒适度（风险）所获得的好奇心（收益）。为评估所提方法，我们新收集了包含用户对日本食物的调研数据及对外国食物舒适度与好奇心的评估数据集。通过与现有方法比较，Wilcoxon符号秩检验表明：当基于口味估算舒适度且基于成分估算好奇心时，基于MDS的方法优于基线方法，而基于KDS的方法未显示显著差异；当基于口味估算好奇心且基于成分估算舒适度时，两种方法均优于基线。在ROC-AUC值方面，基于MDS的方法持续优于KDS方法。