Multi-Source Data Fusion for Anime Pilgrimage Recommendation: Integrating Accessibility, Seasonality, and Popularity

Anime pilgrimage refers to the act of fans visiting real-world locations featured in anime works, offering visual familiarity alongside cultural depth. However, existing studies on anime tourism provide limited computational support for selecting pilgrimage sites based on contextual and experiential factors. This study proposes an intelligent recommendation framework based on multi-source data fusion that integrates three key elements: transportation accessibility, seasonal alignment between the current environment and the anime’s depicted scene, and a Cross-Platform Popularity Index (CPPI) derived from major global platforms. We evaluate each pilgrimage location using route-based accessibility analysis, season-scene discrepancy scoring, and robustly normalized popularity metrics. These factors are combined into a weighted Multi-Criteria Decision Making (MCDM) model to generate context-aware recommendations. To rigorously validate the proposed approach, a user study was conducted using a ranking task involving popular destinations in Tokyo. Participants were presented with travel conditions, spatial relationships, and popularity scores and then asked to rank their preferences. We used standard ranking-based metrics to compare system-generated rankings with participant choices. Furthermore, we conducted an ablation study to quantify the individual contribution of accessibility, seasonality, and popularity. The results demonstrate strong alignment between the model and user preferences, confirming that incorporating these three dimensions significantly enhances the reliability and satisfaction of real-world anime pilgrimage planning.