A Controlled Study of Relevance–Diversity Trade-offs in News Recommendation with Embeddings - Toledo, Ana Lilian Alfonso; Reguera, Vitalio Alfonso; Lunardi, Gabriel Machado (Anais do Seminário Integrado de Software e Hardware (SEMISH), 2026)
News recommender systems reduce information overload, but personalization can reinforce the filter-bubble effect by repeatedly prioritizing similar content. Post-filtering diversification re-ranks top-N lists using item–item similarity, so outcomes depend on item representations. We study how replacing binary news features with TruncatedSVD embeddings affects the relevance–diversity balance in a controlled pipeline. Using a dataset of Brazilian political news, we run offline temporal replay with KNN-U, KNN-I, and SVD plus Maximal Marginal Relevance (MMR), sweeping embedding sizes. Results show embeddings boost neighborhood-based relevance but increase homogeneity, sharpening the relevance–diversity trade-off.