Abstract
Public complaints exhibit strong spatiotemporal dependencies, where issues often propagate across categories within short timeframes, yet existing studies largely overlook cross-category recurrence and underutilize embedding representations for structured data. To address this gap, this study proposes a multilabel embedding-based framework to predict short-term cross-category complaint recurrence using structured spatiotemporal data. Using 48,103 real-world complaint records, the framework integrates embedding representations with Machine Learning (ML) and Deep Learning (DL) models to predict the likelihood of multiple complaint categories recurring within the next seven days in the same area. The results indicated that both approaches achieved average label-wise F1-scores of 30.5–32.6%, exceeding 81% for highly recurrent categories. The best ML model, Binary Relevance with Logistic Regression using multilingual-e5-large embeddings, achieved the lowest Hamming loss of 0.138 ± 0.126. Statistical analysis confirmed non-normality with Shapiro–Wilk statistics between 0.796 and 0.819 and p-values below 0.05, and significant differences across models with a Friedman test statistic of 512.531 and p-values below 0.05, although no significant pairwise differences were found with the Nemenyi post hoc test. Furthermore, 95% bootstrap confidence intervals indicate stable performance, with F1 ranging from 74.1% to 74.9% for ML and 73.2% to 74% for DL.