Emotion-Aware Serendipity Recommendation from Textual Reviews: BERT-Based Emotional Clustering

This study proposes an emotion-aware serendipity recommendation framework based on textual reviews. Six BERT-based binary classifiers are trained to detect surprise, curiosity, trust, nostalgia, frustration, and enchantment from user reviews. The predicted emotion probabilities are used to build stable review-level emotional vectors. Two complementary contributions are introduced. Contribution 1 develops an emotion-aware reranking strategy that combines calibrated emotional information with relevance, novelty, and unexpectedness. Contribution 2 introduces EC-SereRank, an emotional-combination-based reranking model that preserves co-activated emotions within reviews and groups them using K-medoids into interpretable affective meta-groups. Experiments on Amazon Product Reviews show that affective information improves serendipity-oriented recommendation. The ablation study confirms that relevance and unexpectedness alone are not sufficient, while the integration of emotional compatibility improves recommendation quality. EC-SereRank further shows that emotional combinations provide an interpretable complementary signal for affective serendipity recommendation.

@article{Benayad2026,
  title     = {Emotion-Aware Serendipity Recommendation from Textual Reviews: BERT-Based Emotional Clustering},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Mariam Benayad and Ahmed Zellou},
  doi       = {10.14569/IJACSA.2026.0170693},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170693}
}