Reading Emotional Preferences in Adolescents: Emotion-Aware and Scalable Personalization for Book Recommendation

Reading engagement during adolescence is strongly associated with long-term academic and emotional outcomes, yet recent large-scale evidence shows a persistent decline in teenagers’ reading motivation and proficiency. Although personalized recommender systems offer a promising educational technology, most existing book recommenders rely on behavioral logs or demographic assumptions, which are often sparse, biased, and difficult to deploy in privacy-sensitive environments such as schools and libraries. We propose a personalized, emotion-aware book recommendation framework that models adolescents’ individualized preferences using lightweight and interpretable emotional representations derived from publicly available book descriptions. Leveraging the NRC Emotion Intensity Lexicon (NRC-EIL), we embed users and books in a shared affective space and construct personalized target emotion vectors from previously preferred books. We evaluate three ranking strategies: (i) similarity-based retrieval in the emotion space, (ii) a hybrid method that conditions large language model (LLM) ranking on structured emotion vectors, and (iii) an LLM-only baseline without explicit affective modeling. Experiments on the Book-Crossing dataset demonstrate that incorporating structured emotional signals consistently improves ranking effectiveness across P@K, MRR, and NDCG metrics, with statistically significant gains validated using Wilcoxon signed-rank tests. Results further show robustness under cold-start and sparse-interaction settings while maintaining interpretability and avoiding reliance on sensitive user attributes. These findings establish emotion-aware retrieval as a principled and scalable direction for advancing personalized recommendation and supporting sustained teenage reading engagement.