Systematic Evaluation of Top-K Neighborhood in Emotion-Aware Literature Book Recommendation

Literature books recommender systems often overlook the vital affective dimensions that dictate reading preferences. To address this, we present an emotion-aware recommendation framework that leverages latent emotional profiles extracted from user reviews. By constructing an emotional embedding space, the system maps the affective resonance of books and aligns them with historical user preferences. This study specifically investigates the sensitivity of neighborhood size, defined as the number of nearest neighbors (Top-K), and emotional similarity evaluation thresholds (τ) within affective-based collaborative filtering. Through a systematic empirical evaluation across four nested datasets, we demonstrate that affective recommendation alignment is highly dependent on neighborhood size. Our findings identify Top-K=3 as an effective neighborhood parameter, successfully balancing highly affective aligned item acquisition while neutralizing the data noise introduced by larger user clusters. Robustness analysis shows that the proposed framework maintains high affective recommendation alignment under strict similarity constraints.